Overview

Dataset statistics

Number of variables98
Number of observations80899
Missing cells5811180
Missing cells (%)73.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory55.7 MiB
Average record size in memory722.0 B

Variable types

Categorical16
DateTime1
Boolean55
Numeric26

Warnings

bleeding_severe has constant value "True" Constant
bleeding_vaginal has constant value "False" Constant
event_admission has constant value "True" Constant
event_discharge has constant value "True" Constant
event_enrolment has constant value "True" Constant
event_onset has constant value "True" Constant
event_shock has constant value "True" Constant
impairment has constant value "True" Constant
liver_involved has constant value "True" Constant
oedema_pulmonary has constant value "False" Constant
parental_fluid has constant value "True" Constant
pcr_dengue_interpretation has constant value "Lab-confirmed Dengue" Constant
pcr_dengue_serotype has constant value "Constant
perfusion has constant value "True" Constant
respiratory_distress has constant value "True" Constant
shock_multiple has constant value "False" Constant
study_no has a high cardinality: 8107 distinct values High cardinality
icd_code has a high cardinality: 124 distinct values High cardinality
age is highly correlated with heightHigh correlation
alb is highly correlated with bleeding and 2 other fieldsHigh correlation
alt is highly correlated with bleeding and 2 other fieldsHigh correlation
ast is highly correlated with bleedingHigh correlation
bleeding is highly correlated with alb and 12 other fieldsHigh correlation
creatine_kinase is highly correlated with bleeding and 2 other fieldsHigh correlation
gcs_eye_movement is highly correlated with gcs_verbal_responseHigh correlation
gcs_motor_response is highly correlated with gcs_verbal_responseHigh correlation
gcs_verbal_response is highly correlated with gcs_eye_movement and 1 other fieldsHigh correlation
haematocrit_no is highly correlated with alb and 11 other fieldsHigh correlation
haematocrit_percent is highly correlated with bleeding and 2 other fieldsHigh correlation
height is highly correlated with ageHigh correlation
lymphocytes is highly correlated with bleeding and 2 other fieldsHigh correlation
lymphocytes_percent is highly correlated with bleeding and 3 other fieldsHigh correlation
monocytes is highly correlated with bleeding and 2 other fieldsHigh correlation
monocytes_percent is highly correlated with bleeding and 2 other fieldsHigh correlation
neutrophils is highly correlated with bleeding and 3 other fieldsHigh correlation
neutrophils_percent is highly correlated with bleeding and 3 other fieldsHigh correlation
platelet_no is highly correlated with alb and 11 other fieldsHigh correlation
plt is highly correlated with bleeding and 2 other fieldsHigh correlation
wbc is highly correlated with bleeding and 3 other fieldsHigh correlation
abdominal_pain has 45264 (56.0%) missing values Missing
abdominal_tenderness has 72799 (90.0%) missing values Missing
agitated has 80893 (> 99.9%) missing values Missing
alb has 73046 (90.3%) missing values Missing
alt has 72821 (90.0%) missing values Missing
ascites has 79334 (98.1%) missing values Missing
ast has 72822 (90.0%) missing values Missing
bleeding has 52093 (64.4%) missing values Missing
bleeding_gum has 80874 (> 99.9%) missing values Missing
bleeding_nose has 80874 (> 99.9%) missing values Missing
bleeding_severe has 80874 (> 99.9%) missing values Missing
bleeding_vaginal has 80874 (> 99.9%) missing values Missing
bleeding_vensite has 80874 (> 99.9%) missing values Missing
body_temperature has 49316 (61.0%) missing values Missing
breath has 53364 (66.0%) missing values Missing
care_type has 79334 (98.1%) missing values Missing
cns_abnormal has 80893 (> 99.9%) missing values Missing
cns_abnormal_signs has 80894 (> 99.9%) missing values Missing
compression has 79334 (98.1%) missing values Missing
conjunctival_injection has 72799 (90.0%) missing values Missing
creatine_kinase has 72819 (90.0%) missing values Missing
cryoprecipitate has 79334 (98.1%) missing values Missing
crystalloid has 79334 (98.1%) missing values Missing
dehydration has 79334 (98.1%) missing values Missing
diarrhoea has 72800 (90.0%) missing values Missing
event_admission has 79305 (98.0%) missing values Missing
event_discharge has 79334 (98.1%) missing values Missing
event_enrolment has 72799 (90.0%) missing values Missing
event_onset has 72799 (90.0%) missing values Missing
event_shock has 80785 (99.9%) missing values Missing
ffp has 79334 (98.1%) missing values Missing
gum_packing has 79334 (98.1%) missing values Missing
haematocrit_high has 79334 (98.1%) missing values Missing
haematocrit_max has 79335 (98.1%) missing values Missing
haematocrit_no has 79334 (98.1%) missing values Missing
haematocrit_percent has 72803 (90.0%) missing values Missing
hepatomegaly has 72799 (90.0%) missing values Missing
icd_code has 77537 (95.8%) missing values Missing
igg has 76839 (95.0%) missing values Missing
igg_interpretation has 76839 (95.0%) missing values Missing
igm has 76839 (95.0%) missing values Missing
igm_interpretation has 76839 (95.0%) missing values Missing
impairment has 80897 (> 99.9%) missing values Missing
jaundice has 80885 (> 99.9%) missing values Missing
lathargy_severe has 53364 (66.0%) missing values Missing
liver_acute has 80884 (> 99.9%) missing values Missing
liver_involved has 80888 (> 99.9%) missing values Missing
lymphocytes has 72803 (90.0%) missing values Missing
lymphocytes_percent has 72827 (90.0%) missing values Missing
monocytes has 72803 (90.0%) missing values Missing
monocytes_percent has 72803 (90.0%) missing values Missing
movement has 75299 (93.1%) missing values Missing
nasal_packing has 79334 (98.1%) missing values Missing
neutrophils has 72804 (90.0%) missing values Missing
neutrophils_percent has 72827 (90.0%) missing values Missing
ns1_platelia_analyte_interpretation has 75015 (92.7%) missing values Missing
oedema_pulmonary has 79334 (98.1%) missing values Missing
outcome has 68422 (84.6%) missing values Missing
parental_fluid has 80684 (99.7%) missing values Missing
pcr_dengue_load has 72800 (90.0%) missing values Missing
perfusion has 80785 (99.9%) missing values Missing
platelet_min has 79336 (98.1%) missing values Missing
platelet_no has 79334 (98.1%) missing values Missing
platelets has 79334 (98.1%) missing values Missing
pleural_effusion has 79334 (98.1%) missing values Missing
plt has 72803 (90.0%) missing values Missing
pulse has 80787 (99.9%) missing values Missing
pulse_status has 80785 (99.9%) missing values Missing
rbc has 79334 (98.1%) missing values Missing
respiratory_distress has 80866 (> 99.9%) missing values Missing
restlessness has 53364 (66.0%) missing values Missing
sbp has 80789 (99.9%) missing values Missing
serology_interpretation has 78859 (97.5%) missing values Missing
skin_clammy has 53364 (66.0%) missing values Missing
skin_flush has 72799 (90.0%) missing values Missing
skin_rash has 72799 (90.0%) missing values Missing
vomiting has 43727 (54.1%) missing values Missing
wbc has 72803 (90.0%) missing values Missing
haematocrit_max is highly skewed (γ1 = 28.55880417) Skewed
monocytes is highly skewed (γ1 = 88.04653391) Skewed
pcr_dengue_load is highly skewed (γ1 = 41.74880205) Skewed
platelet_min is highly skewed (γ1 = 39.25354873) Skewed
study_no is uniformly distributed Uniform
pcr_dengue_load has 6032 (7.5%) zeros Zeros

Reproduction

Analysis started2021-01-24 11:13:02.474213
Analysis finished2021-01-24 11:15:31.190307
Duration2 minutes and 28.72 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

study_no
Categorical

HIGH CARDINALITY
UNIFORM

Distinct8107
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size632.1 KiB
3-0050
 
17
16-0406
 
17
6-0259
 
17
1-0018
 
16
2-0503
 
16
Other values (8102)
80816 

Length

Max length7
Median length6
Mean length6.165193637
Min length5

Characters and Unicode

Total characters498758
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st row1-0001
2nd row1-0001
3rd row1-0001
4th row1-0001
5th row1-0001
ValueCountFrequency (%)
3-005017
 
< 0.1%
16-040617
 
< 0.1%
6-025917
 
< 0.1%
1-001816
 
< 0.1%
2-050316
 
< 0.1%
1-212716
 
< 0.1%
3-004716
 
< 0.1%
3-117116
 
< 0.1%
6-057216
 
< 0.1%
6-064616
 
< 0.1%
Other values (8097)80736
99.8%
2021-01-24T12:15:31.416801image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3-005017
 
< 0.1%
16-040617
 
< 0.1%
6-025917
 
< 0.1%
1-001816
 
< 0.1%
2-050316
 
< 0.1%
1-212716
 
< 0.1%
3-004716
 
< 0.1%
3-117116
 
< 0.1%
6-057216
 
< 0.1%
6-064616
 
< 0.1%
Other values (8097)80736
99.8%

Most occurring characters

ValueCountFrequency (%)
086702
17.4%
181485
16.3%
-80899
16.2%
341270
8.3%
640970
8.2%
239073
7.8%
435852
7.2%
528716
 
5.8%
721856
 
4.4%
821296
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number417859
83.8%
Dash Punctuation80899
 
16.2%

Most frequent character per category

ValueCountFrequency (%)
086702
20.7%
181485
19.5%
341270
9.9%
640970
9.8%
239073
9.4%
435852
8.6%
528716
 
6.9%
721856
 
5.2%
821296
 
5.1%
920639
 
4.9%
ValueCountFrequency (%)
-80899
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common498758
100.0%

Most frequent character per script

ValueCountFrequency (%)
086702
17.4%
181485
16.3%
-80899
16.2%
341270
8.3%
640970
8.2%
239073
7.8%
435852
7.2%
528716
 
5.8%
721856
 
4.4%
821296
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII498758
100.0%

Most frequent character per block

ValueCountFrequency (%)
086702
17.4%
181485
16.3%
-80899
16.2%
341270
8.3%
640970
8.2%
239073
7.8%
435852
7.2%
528716
 
5.8%
721856
 
4.4%
821296
 
4.3%

date
Date

Distinct24935
Distinct (%)30.8%
Missing3
Missing (%)< 0.1%
Memory size632.1 KiB
Minimum2010-10-17 05:00:00
Maximum2014-12-18 00:00:00
2021-01-24T12:15:31.528909image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:31.663010image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

abdominal_pain
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing45264
Missing (%)56.0%
Memory size632.1 KiB
False
33873 
True
 
1762
(Missing)
45264 
ValueCountFrequency (%)
False33873
41.9%
True1762
 
2.2%
(Missing)45264
56.0%
2021-01-24T12:15:31.741343image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

abdominal_tenderness
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing72799
Missing (%)90.0%
Memory size632.1 KiB
False
7963 
True
 
137
(Missing)
72799 
ValueCountFrequency (%)
False7963
 
9.8%
True137
 
0.2%
(Missing)72799
90.0%
2021-01-24T12:15:31.778581image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct16
Distinct (%)< 0.1%
Missing96
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean6.366570548
Minimum0
Maximum15
Zeros38
Zeros (%)< 0.1%
Memory size632.1 KiB
2021-01-24T12:15:31.841848image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q39
95-th percentile13
Maximum15
Range15
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.628176063
Coefficient of variation (CV)0.5698791894
Kurtosis-0.7738946349
Mean6.366570548
Median Absolute Deviation (MAD)3
Skewness0.393442241
Sum514438
Variance13.16366155
MonotocityNot monotonic
2021-01-24T12:15:31.933269image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
58262
10.2%
47994
9.9%
37940
9.8%
27492
9.3%
76938
8.6%
66766
8.4%
86348
7.8%
15814
7.2%
95657
7.0%
104846
 
6.0%
Other values (6)12746
15.8%
ValueCountFrequency (%)
038
 
< 0.1%
15814
7.2%
27492
9.3%
37940
9.8%
47994
9.9%
ValueCountFrequency (%)
15715
 
0.9%
142019
2.5%
132675
3.3%
123160
3.9%
114139
5.1%

agitated
Boolean

MISSING

Distinct2
Distinct (%)33.3%
Missing80893
Missing (%)> 99.9%
Memory size632.1 KiB
False
 
4
True
 
2
(Missing)
80893 
ValueCountFrequency (%)
False4
 
< 0.1%
True2
 
< 0.1%
(Missing)80893
> 99.9%
2021-01-24T12:15:31.994747image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

alb
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct174
Distinct (%)2.2%
Missing73046
Missing (%)90.3%
Infinite0
Infinite (%)0.0%
Mean43.86689163
Minimum14.8
Maximum50
Zeros0
Zeros (%)0.0%
Memory size632.1 KiB
2021-01-24T12:15:32.080408image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum14.8
5-th percentile39
Q142
median44
Q345.9
95-th percentile48.4
Maximum50
Range35.2
Interquartile range (IQR)3.9

Descriptive statistics

Standard deviation2.911434495
Coefficient of variation (CV)0.0663697469
Kurtosis1.754940441
Mean43.86689163
Median Absolute Deviation (MAD)2
Skewness-0.5509295499
Sum344486.7
Variance8.476450818
MonotocityNot monotonic
2021-01-24T12:15:32.207222image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44.7138
 
0.2%
44.1134
 
0.2%
43120
 
0.1%
44118
 
0.1%
45.3116
 
0.1%
44.4115
 
0.1%
44.9114
 
0.1%
43.8112
 
0.1%
43.3111
 
0.1%
44.5110
 
0.1%
Other values (164)6665
 
8.2%
(Missing)73046
90.3%
ValueCountFrequency (%)
14.81
< 0.1%
27.11
< 0.1%
29.31
< 0.1%
29.61
< 0.1%
301
< 0.1%
ValueCountFrequency (%)
5019
< 0.1%
49.916
< 0.1%
49.818
< 0.1%
49.723
< 0.1%
49.613
< 0.1%

alt
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct199
Distinct (%)2.5%
Missing72821
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean23.35241396
Minimum1
Maximum594
Zeros0
Zeros (%)0.0%
Memory size632.1 KiB
2021-01-24T12:15:32.328294image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q113
median17
Q323
95-th percentile54
Maximum594
Range593
Interquartile range (IQR)10

Descriptive statistics

Standard deviation27.36554273
Coefficient of variation (CV)1.171850704
Kurtosis99.84896215
Mean23.35241396
Median Absolute Deviation (MAD)5
Skewness8.126744215
Sum188640.8
Variance748.872929
MonotocityNot monotonic
2021-01-24T12:15:32.446025image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16561
 
0.7%
14548
 
0.7%
15539
 
0.7%
17500
 
0.6%
12499
 
0.6%
13498
 
0.6%
18429
 
0.5%
19379
 
0.5%
11354
 
0.4%
20289
 
0.4%
Other values (189)3482
 
4.3%
(Missing)72821
90.0%
ValueCountFrequency (%)
13
 
< 0.1%
25
 
< 0.1%
314
< 0.1%
412
 
< 0.1%
530
< 0.1%
ValueCountFrequency (%)
5941
< 0.1%
5681
< 0.1%
4511
< 0.1%
4321
< 0.1%
3961
< 0.1%

anorexia
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.1 KiB
False
80482 
True
 
417
ValueCountFrequency (%)
False80482
99.5%
True417
 
0.5%
2021-01-24T12:15:32.514318image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

ascites
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing79334
Missing (%)98.1%
Memory size632.1 KiB
False
 
1542
True
 
23
(Missing)
79334 
ValueCountFrequency (%)
False1542
 
1.9%
True23
 
< 0.1%
(Missing)79334
98.1%
2021-01-24T12:15:32.551418image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

ast
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct243
Distinct (%)3.0%
Missing72822
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean49.37620404
Minimum9
Maximum790
Zeros0
Zeros (%)0.0%
Memory size632.1 KiB
2021-01-24T12:15:32.631540image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile27
Q136
median43
Q353
95-th percentile86
Maximum790
Range781
Interquartile range (IQR)17

Descriptive statistics

Standard deviation33.71217449
Coefficient of variation (CV)0.6827615681
Kurtosis103.921963
Mean49.37620404
Median Absolute Deviation (MAD)8
Skewness8.034287307
Sum398811.6
Variance1136.510709
MonotocityNot monotonic
2021-01-24T12:15:32.748143image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39298
 
0.4%
41295
 
0.4%
42289
 
0.4%
36286
 
0.4%
44279
 
0.3%
40270
 
0.3%
37261
 
0.3%
43257
 
0.3%
35256
 
0.3%
45256
 
0.3%
Other values (233)5330
 
6.6%
(Missing)72822
90.0%
ValueCountFrequency (%)
91
 
< 0.1%
111
 
< 0.1%
132
< 0.1%
141
 
< 0.1%
153
< 0.1%
ValueCountFrequency (%)
7901
< 0.1%
7531
< 0.1%
5521
< 0.1%
5441
< 0.1%
4941
< 0.1%

bleeding
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing52093
Missing (%)64.4%
Memory size158.1 KiB
False
27440 
True
 
1366
(Missing)
52093 
ValueCountFrequency (%)
False27440
33.9%
True1366
 
1.7%
(Missing)52093
64.4%
2021-01-24T12:15:32.820017image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.1 KiB
False
80881 
True
 
18
ValueCountFrequency (%)
False80881
> 99.9%
True18
 
< 0.1%
2021-01-24T12:15:32.858861image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

bleeding_gum
Boolean

MISSING

Distinct2
Distinct (%)8.0%
Missing80874
Missing (%)> 99.9%
Memory size632.1 KiB
False
 
21
True
 
4
(Missing)
80874 
ValueCountFrequency (%)
False21
 
< 0.1%
True4
 
< 0.1%
(Missing)80874
> 99.9%
2021-01-24T12:15:32.893612image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.1 KiB
False
80363 
True
 
536
ValueCountFrequency (%)
False80363
99.3%
True536
 
0.7%
2021-01-24T12:15:32.927764image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

bleeding_nose
Boolean

MISSING

Distinct2
Distinct (%)8.0%
Missing80874
Missing (%)> 99.9%
Memory size632.1 KiB
False
 
17
True
 
8
(Missing)
80874 
ValueCountFrequency (%)
False17
 
< 0.1%
True8
 
< 0.1%
(Missing)80874
> 99.9%
2021-01-24T12:15:32.962803image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

bleeding_severe
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)4.0%
Missing80874
Missing (%)> 99.9%
Memory size632.1 KiB
True
 
25
(Missing)
80874 
ValueCountFrequency (%)
True25
 
< 0.1%
(Missing)80874
> 99.9%
2021-01-24T12:15:32.995219image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.1 KiB
False
79899 
True
 
1000
ValueCountFrequency (%)
False79899
98.8%
True1000
 
1.2%
2021-01-24T12:15:33.023771image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.1 KiB
False
80898 
True
 
1
ValueCountFrequency (%)
False80898
> 99.9%
True1
 
< 0.1%
2021-01-24T12:15:33.056542image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

bleeding_vaginal
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)4.0%
Missing80874
Missing (%)> 99.9%
Memory size632.1 KiB
False
 
25
(Missing)
80874 
ValueCountFrequency (%)
False25
 
< 0.1%
(Missing)80874
> 99.9%
2021-01-24T12:15:33.087621image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

bleeding_vensite
Boolean

MISSING

Distinct2
Distinct (%)8.0%
Missing80874
Missing (%)> 99.9%
Memory size632.1 KiB
False
 
22
True
 
3
(Missing)
80874 
ValueCountFrequency (%)
False22
 
< 0.1%
True3
 
< 0.1%
(Missing)80874
> 99.9%
2021-01-24T12:15:33.117108image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

body_temperature
Real number (ℝ≥0)

MISSING

Distinct79
Distinct (%)0.3%
Missing49316
Missing (%)61.0%
Infinite0
Infinite (%)0.0%
Mean37.38284457
Minimum35
Maximum41.3
Zeros0
Zeros (%)0.0%
Memory size632.1 KiB
2021-01-24T12:15:33.192774image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile36.3
Q136.8
median37
Q338
95-th percentile39
Maximum41.3
Range6.3
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation0.9018707617
Coefficient of variation (CV)0.02412525778
Kurtosis0.317256728
Mean37.38284457
Median Absolute Deviation (MAD)0.5
Skewness0.9222488968
Sum1180662.38
Variance0.8133708709
MonotocityNot monotonic
2021-01-24T12:15:33.307907image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
377863
 
9.7%
36.52444
 
3.0%
36.82263
 
2.8%
381824
 
2.3%
37.51500
 
1.9%
36.71261
 
1.6%
36.91064
 
1.3%
391059
 
1.3%
38.51021
 
1.3%
36961
 
1.2%
Other values (69)10323
 
12.8%
(Missing)49316
61.0%
ValueCountFrequency (%)
353
 
< 0.1%
35.26
 
< 0.1%
35.32
 
< 0.1%
35.44
 
< 0.1%
35.534
< 0.1%
ValueCountFrequency (%)
41.31
 
< 0.1%
41.12
 
< 0.1%
413
< 0.1%
40.95
< 0.1%
40.84
< 0.1%

breath
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing53364
Missing (%)66.0%
Memory size632.1 KiB
False
27526 
True
 
9
(Missing)
53364 
ValueCountFrequency (%)
False27526
34.0%
True9
 
< 0.1%
(Missing)53364
66.0%
2021-01-24T12:15:33.373846image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

care_type
Categorical

MISSING

Distinct3
Distinct (%)0.2%
Missing79334
Missing (%)98.1%
Memory size632.1 KiB
Level 3
1010 
Level 2
451 
Level 1
104 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters10955
Distinct characters8
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLevel 1
2nd rowLevel 3
3rd rowLevel 3
4th rowLevel 2
5th rowLevel 3
ValueCountFrequency (%)
Level 31010
 
1.2%
Level 2451
 
0.6%
Level 1104
 
0.1%
(Missing)79334
98.1%
2021-01-24T12:15:33.530317image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T12:15:33.586620image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
level1565
50.0%
31010
32.3%
2451
 
14.4%
1104
 
3.3%

Most occurring characters

ValueCountFrequency (%)
e3130
28.6%
L1565
14.3%
v1565
14.3%
l1565
14.3%
1565
14.3%
31010
 
9.2%
2451
 
4.1%
1104
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6260
57.1%
Uppercase Letter1565
 
14.3%
Space Separator1565
 
14.3%
Decimal Number1565
 
14.3%

Most frequent character per category

ValueCountFrequency (%)
e3130
50.0%
v1565
25.0%
l1565
25.0%
ValueCountFrequency (%)
31010
64.5%
2451
28.8%
1104
 
6.6%
ValueCountFrequency (%)
L1565
100.0%
ValueCountFrequency (%)
1565
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7825
71.4%
Common3130
 
28.6%

Most frequent character per script

ValueCountFrequency (%)
e3130
40.0%
L1565
20.0%
v1565
20.0%
l1565
20.0%
ValueCountFrequency (%)
1565
50.0%
31010
32.3%
2451
 
14.4%
1104
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII10955
100.0%

Most frequent character per block

ValueCountFrequency (%)
e3130
28.6%
L1565
14.3%
v1565
14.3%
l1565
14.3%
1565
14.3%
31010
 
9.2%
2451
 
4.1%
1104
 
0.9%

cns_abnormal
Boolean

MISSING

Distinct2
Distinct (%)33.3%
Missing80893
Missing (%)> 99.9%
Memory size632.1 KiB
True
 
5
False
 
1
(Missing)
80893 
ValueCountFrequency (%)
True5
 
< 0.1%
False1
 
< 0.1%
(Missing)80893
> 99.9%
2021-01-24T12:15:33.627384image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

cns_abnormal_signs
Boolean

MISSING

Distinct2
Distinct (%)40.0%
Missing80894
Missing (%)> 99.9%
Memory size632.1 KiB
False
 
4
True
 
1
(Missing)
80894 
ValueCountFrequency (%)
False4
 
< 0.1%
True1
 
< 0.1%
(Missing)80894
> 99.9%
2021-01-24T12:15:33.663138image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

compression
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing79334
Missing (%)98.1%
Memory size632.1 KiB
False
 
1561
True
 
4
(Missing)
79334 
ValueCountFrequency (%)
False1561
 
1.9%
True4
 
< 0.1%
(Missing)79334
98.1%
2021-01-24T12:15:33.697510image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing72799
Missing (%)90.0%
Memory size632.1 KiB
False
 
7276
True
 
824
(Missing)
72799 
ValueCountFrequency (%)
False7276
 
9.0%
True824
 
1.0%
(Missing)72799
90.0%
2021-01-24T12:15:33.731742image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

creatine_kinase
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct522
Distinct (%)6.5%
Missing72819
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean129.4112624
Minimum2
Maximum4677
Zeros0
Zeros (%)0.0%
Memory size632.1 KiB
2021-01-24T12:15:33.805511image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile54
Q178
median102
Q3134
95-th percentile251.05
Maximum4677
Range4675
Interquartile range (IQR)56

Descriptive statistics

Standard deviation165.1880551
Coefficient of variation (CV)1.276458108
Kurtosis258.2030258
Mean129.4112624
Median Absolute Deviation (MAD)27
Skewness13.08542589
Sum1045643
Variance27287.09356
MonotocityNot monotonic
2021-01-24T12:15:33.922174image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112107
 
0.1%
92103
 
0.1%
94101
 
0.1%
88100
 
0.1%
7899
 
0.1%
9194
 
0.1%
7792
 
0.1%
9792
 
0.1%
10091
 
0.1%
8189
 
0.1%
Other values (512)7112
 
8.8%
(Missing)72819
90.0%
ValueCountFrequency (%)
21
< 0.1%
41
< 0.1%
51
< 0.1%
131
< 0.1%
171
< 0.1%
ValueCountFrequency (%)
46771
< 0.1%
42341
< 0.1%
41881
< 0.1%
38411
< 0.1%
34611
< 0.1%

cryoprecipitate
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing79334
Missing (%)98.1%
Memory size632.1 KiB
False
 
1562
True
 
3
(Missing)
79334 
ValueCountFrequency (%)
False1562
 
1.9%
True3
 
< 0.1%
(Missing)79334
98.1%
2021-01-24T12:15:33.998215image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

crystalloid
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing79334
Missing (%)98.1%
Memory size632.1 KiB
False
 
1552
True
 
13
(Missing)
79334 
ValueCountFrequency (%)
False1552
 
1.9%
True13
 
< 0.1%
(Missing)79334
98.1%
2021-01-24T12:15:34.033878image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

dbp
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size632.1 KiB
nan
80877 
60.0
 
18
50.0
 
3
40.0
 
1

Length

Max length4
Median length3
Mean length3.000271944
Min length3

Characters and Unicode

Total characters242719
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan80877
> 99.9%
60.018
 
< 0.1%
50.03
 
< 0.1%
40.01
 
< 0.1%
2021-01-24T12:15:34.207909image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T12:15:34.265892image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan80877
> 99.9%
60.018
 
< 0.1%
50.03
 
< 0.1%
40.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n161754
66.6%
a80877
33.3%
044
 
< 0.1%
.22
 
< 0.1%
618
 
< 0.1%
53
 
< 0.1%
41
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter242631
> 99.9%
Decimal Number66
 
< 0.1%
Other Punctuation22
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
044
66.7%
618
27.3%
53
 
4.5%
41
 
1.5%
ValueCountFrequency (%)
n161754
66.7%
a80877
33.3%
ValueCountFrequency (%)
.22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin242631
> 99.9%
Common88
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
044
50.0%
.22
25.0%
618
20.5%
53
 
3.4%
41
 
1.1%
ValueCountFrequency (%)
n161754
66.7%
a80877
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII242719
100.0%

Most frequent character per block

ValueCountFrequency (%)
n161754
66.6%
a80877
33.3%
044
 
< 0.1%
.22
 
< 0.1%
618
 
< 0.1%
53
 
< 0.1%
41
 
< 0.1%

dehydration
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing79334
Missing (%)98.1%
Memory size632.1 KiB
False
 
1516
True
 
49
(Missing)
79334 
ValueCountFrequency (%)
False1516
 
1.9%
True49
 
0.1%
(Missing)79334
98.1%
2021-01-24T12:15:34.311609image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

diarrhoea
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing72800
Missing (%)90.0%
Memory size632.1 KiB
False
7638 
True
 
461
(Missing)
72800 
ValueCountFrequency (%)
False7638
 
9.4%
True461
 
0.6%
(Missing)72800
90.0%
2021-01-24T12:15:34.351064image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

event_admission
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)0.1%
Missing79305
Missing (%)98.0%
Memory size632.1 KiB
True
 
1594
(Missing)
79305 
ValueCountFrequency (%)
True1594
 
2.0%
(Missing)79305
98.0%
2021-01-24T12:15:34.384001image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

event_discharge
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)0.1%
Missing79334
Missing (%)98.1%
Memory size632.1 KiB
True
 
1565
(Missing)
79334 
ValueCountFrequency (%)
True1565
 
1.9%
(Missing)79334
98.1%
2021-01-24T12:15:34.413514image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

event_enrolment
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing72799
Missing (%)90.0%
Memory size632.1 KiB
True
8100 
(Missing)
72799 
ValueCountFrequency (%)
True8100
 
10.0%
(Missing)72799
90.0%
2021-01-24T12:15:34.442267image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

event_onset
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing72799
Missing (%)90.0%
Memory size632.1 KiB
True
8100 
(Missing)
72799 
ValueCountFrequency (%)
True8100
 
10.0%
(Missing)72799
90.0%
2021-01-24T12:15:34.471375image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

event_shock
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)0.9%
Missing80785
Missing (%)99.9%
Memory size632.1 KiB
True
 
114
(Missing)
80785 
ValueCountFrequency (%)
True114
 
0.1%
(Missing)80785
99.9%
2021-01-24T12:15:34.500170image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

ffp
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing79334
Missing (%)98.1%
Memory size632.1 KiB
False
 
1553
True
 
12
(Missing)
79334 
ValueCountFrequency (%)
False1553
 
1.9%
True12
 
< 0.1%
(Missing)79334
98.1%
2021-01-24T12:15:34.529024image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

gcs_eye_movement
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size632.1 KiB
nan
80894 
1.0
 
3
4.0
 
1
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters242697
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan80894
> 99.9%
1.03
 
< 0.1%
4.01
 
< 0.1%
3.01
 
< 0.1%
2021-01-24T12:15:34.697801image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T12:15:34.754923image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan80894
> 99.9%
1.03
 
< 0.1%
4.01
 
< 0.1%
3.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n161788
66.7%
a80894
33.3%
.5
 
< 0.1%
05
 
< 0.1%
13
 
< 0.1%
41
 
< 0.1%
31
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter242682
> 99.9%
Decimal Number10
 
< 0.1%
Other Punctuation5
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
05
50.0%
13
30.0%
41
 
10.0%
31
 
10.0%
ValueCountFrequency (%)
n161788
66.7%
a80894
33.3%
ValueCountFrequency (%)
.5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin242682
> 99.9%
Common15
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
.5
33.3%
05
33.3%
13
20.0%
41
 
6.7%
31
 
6.7%
ValueCountFrequency (%)
n161788
66.7%
a80894
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII242697
100.0%

Most frequent character per block

ValueCountFrequency (%)
n161788
66.7%
a80894
33.3%
.5
 
< 0.1%
05
 
< 0.1%
13
 
< 0.1%
41
 
< 0.1%
31
 
< 0.1%

gcs_motor_response
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size632.1 KiB
nan
80894 
1.0
 
2
5.0
 
1
2.0
 
1
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters242697
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan80894
> 99.9%
1.02
 
< 0.1%
5.01
 
< 0.1%
2.01
 
< 0.1%
4.01
 
< 0.1%
2021-01-24T12:15:34.936735image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T12:15:34.997673image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan80894
> 99.9%
1.02
 
< 0.1%
5.01
 
< 0.1%
2.01
 
< 0.1%
4.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n161788
66.7%
a80894
33.3%
.5
 
< 0.1%
05
 
< 0.1%
12
 
< 0.1%
21
 
< 0.1%
41
 
< 0.1%
51
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter242682
> 99.9%
Decimal Number10
 
< 0.1%
Other Punctuation5
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
05
50.0%
12
 
20.0%
21
 
10.0%
41
 
10.0%
51
 
10.0%
ValueCountFrequency (%)
n161788
66.7%
a80894
33.3%
ValueCountFrequency (%)
.5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin242682
> 99.9%
Common15
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
.5
33.3%
05
33.3%
12
 
13.3%
21
 
6.7%
41
 
6.7%
51
 
6.7%
ValueCountFrequency (%)
n161788
66.7%
a80894
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII242697
100.0%

Most frequent character per block

ValueCountFrequency (%)
n161788
66.7%
a80894
33.3%
.5
 
< 0.1%
05
 
< 0.1%
12
 
< 0.1%
21
 
< 0.1%
41
 
< 0.1%
51
 
< 0.1%

gcs_verbal_response
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size632.1 KiB
nan
80894 
1.0
 
3
5.0
 
1
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters242697
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan80894
> 99.9%
1.03
 
< 0.1%
5.01
 
< 0.1%
4.01
 
< 0.1%
2021-01-24T12:15:35.195908image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T12:15:35.257945image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan80894
> 99.9%
1.03
 
< 0.1%
5.01
 
< 0.1%
4.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n161788
66.7%
a80894
33.3%
.5
 
< 0.1%
05
 
< 0.1%
13
 
< 0.1%
41
 
< 0.1%
51
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter242682
> 99.9%
Decimal Number10
 
< 0.1%
Other Punctuation5
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
05
50.0%
13
30.0%
41
 
10.0%
51
 
10.0%
ValueCountFrequency (%)
n161788
66.7%
a80894
33.3%
ValueCountFrequency (%)
.5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin242682
> 99.9%
Common15
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
.5
33.3%
05
33.3%
13
20.0%
41
 
6.7%
51
 
6.7%
ValueCountFrequency (%)
n161788
66.7%
a80894
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII242697
100.0%

Most frequent character per block

ValueCountFrequency (%)
n161788
66.7%
a80894
33.3%
.5
 
< 0.1%
05
 
< 0.1%
13
 
< 0.1%
41
 
< 0.1%
51
 
< 0.1%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing7
Missing (%)< 0.1%
Memory size632.1 KiB
Male
45493 
Female
35399 

Length

Max length6
Median length4
Mean length4.875216338
Min length4

Characters and Unicode

Total characters394366
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale
ValueCountFrequency (%)
Male45493
56.2%
Female35399
43.8%
(Missing)7
 
< 0.1%
2021-01-24T12:15:35.423054image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T12:15:35.486782image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
male45493
56.2%
female35399
43.8%

Most occurring characters

ValueCountFrequency (%)
e116291
29.5%
a80892
20.5%
l80892
20.5%
M45493
 
11.5%
F35399
 
9.0%
m35399
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter313474
79.5%
Uppercase Letter80892
 
20.5%

Most frequent character per category

ValueCountFrequency (%)
e116291
37.1%
a80892
25.8%
l80892
25.8%
m35399
 
11.3%
ValueCountFrequency (%)
M45493
56.2%
F35399
43.8%

Most occurring scripts

ValueCountFrequency (%)
Latin394366
100.0%

Most frequent character per script

ValueCountFrequency (%)
e116291
29.5%
a80892
20.5%
l80892
20.5%
M45493
 
11.5%
F35399
 
9.0%
m35399
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII394366
100.0%

Most frequent character per block

ValueCountFrequency (%)
e116291
29.5%
a80892
20.5%
l80892
20.5%
M45493
 
11.5%
F35399
 
9.0%
m35399
 
9.0%

gum_packing
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing79334
Missing (%)98.1%
Memory size632.1 KiB
False
 
1562
True
 
3
(Missing)
79334 
ValueCountFrequency (%)
False1562
 
1.9%
True3
 
< 0.1%
(Missing)79334
98.1%
2021-01-24T12:15:35.523211image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

haematocrit_high
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing79334
Missing (%)98.1%
Memory size632.1 KiB
False
 
1454
True
 
111
(Missing)
79334 
ValueCountFrequency (%)
False1454
 
1.8%
True111
 
0.1%
(Missing)79334
98.1%
2021-01-24T12:15:35.560288image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

haematocrit_max
Real number (ℝ≥0)

MISSING
SKEWED

Distinct35
Distinct (%)2.2%
Missing79335
Missing (%)98.1%
Infinite0
Infinite (%)0.0%
Mean41.68734015
Minimum12
Maximum417
Zeros0
Zeros (%)0.0%
Memory size632.1 KiB
2021-01-24T12:15:35.633319image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile35
Q138.75
median41
Q344
95-th percentile50
Maximum417
Range405
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation10.58530559
Coefficient of variation (CV)0.2539213476
Kurtosis1012.169675
Mean41.68734015
Median Absolute Deviation (MAD)3
Skewness28.55880417
Sum65199
Variance112.0486944
MonotocityNot monotonic
2021-01-24T12:15:35.749628image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
40162
 
0.2%
39147
 
0.2%
41138
 
0.2%
42134
 
0.2%
44133
 
0.2%
38111
 
0.1%
43107
 
0.1%
4588
 
0.1%
3784
 
0.1%
3663
 
0.1%
Other values (25)397
 
0.5%
(Missing)79335
98.1%
ValueCountFrequency (%)
121
< 0.1%
211
< 0.1%
281
< 0.1%
292
< 0.1%
301
< 0.1%
ValueCountFrequency (%)
4171
 
< 0.1%
602
< 0.1%
581
 
< 0.1%
571
 
< 0.1%
563
< 0.1%

haematocrit_no
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct32
Distinct (%)2.0%
Missing79334
Missing (%)98.1%
Infinite0
Infinite (%)0.0%
Mean5.863258786
Minimum1
Maximum44
Zeros0
Zeros (%)0.0%
Memory size632.1 KiB
2021-01-24T12:15:35.864199image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q38
95-th percentile17
Maximum44
Range43
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.64841248
Coefficient of variation (CV)0.9633571852
Kurtosis4.910622568
Mean5.863258786
Median Absolute Deviation (MAD)2
Skewness1.922265806
Sum9176
Variance31.90456354
MonotocityNot monotonic
2021-01-24T12:15:35.970334image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1282
 
0.3%
2245
 
0.3%
3190
 
0.2%
4174
 
0.2%
5121
 
0.1%
690
 
0.1%
759
 
0.1%
948
 
0.1%
843
 
0.1%
1141
 
0.1%
Other values (22)272
 
0.3%
(Missing)79334
98.1%
ValueCountFrequency (%)
1282
0.3%
2245
0.3%
3190
0.2%
4174
0.2%
5121
0.1%
ValueCountFrequency (%)
441
 
< 0.1%
393
< 0.1%
321
 
< 0.1%
301
 
< 0.1%
292
< 0.1%

haematocrit_percent
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct239
Distinct (%)3.0%
Missing72803
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean37.86560894
Minimum20.4
Maximum52
Zeros0
Zeros (%)0.0%
Memory size632.1 KiB
2021-01-24T12:15:36.080985image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum20.4
5-th percentile32.6
Q135.6
median37.7
Q339.9
95-th percentile43.7
Maximum52
Range31.6
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation3.415696565
Coefficient of variation (CV)0.09020577407
Kurtosis0.6452218443
Mean37.86560894
Median Absolute Deviation (MAD)2.2
Skewness0.2281996327
Sum306559.97
Variance11.66698303
MonotocityNot monotonic
2021-01-24T12:15:36.208140image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5122
 
0.2%
37.1116
 
0.1%
37.4116
 
0.1%
36.8114
 
0.1%
37113
 
0.1%
39.1112
 
0.1%
37.8109
 
0.1%
37.2106
 
0.1%
37.7104
 
0.1%
38.899
 
0.1%
Other values (229)6985
 
8.6%
(Missing)72803
90.0%
ValueCountFrequency (%)
20.41
< 0.1%
22.51
< 0.1%
241
< 0.1%
24.61
< 0.1%
24.81
< 0.1%
ValueCountFrequency (%)
521
< 0.1%
51.81
< 0.1%
50.92
< 0.1%
50.71
< 0.1%
50.62
< 0.1%

height
Real number (ℝ≥0)

HIGH CORRELATION

Distinct119
Distinct (%)0.1%
Missing7
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean118.2992509
Minimum58
Maximum183
Zeros0
Zeros (%)0.0%
Memory size632.1 KiB
2021-01-24T12:15:36.319874image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum58
5-th percentile81
Q1100
median118
Q3135
95-th percentile157
Maximum183
Range125
Interquartile range (IQR)35

Descriptive statistics

Standard deviation23.01262707
Coefficient of variation (CV)0.1945289332
Kurtosis-0.72181326
Mean118.2992509
Median Absolute Deviation (MAD)18
Skewness0.0764355533
Sum9569463
Variance529.5810045
MonotocityNot monotonic
2021-01-24T12:15:36.447289image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1201883
 
2.3%
1151836
 
2.3%
1101687
 
2.1%
1401553
 
1.9%
1251493
 
1.8%
1301424
 
1.8%
1181403
 
1.7%
1281340
 
1.7%
1351336
 
1.7%
1001330
 
1.6%
Other values (109)65607
81.1%
ValueCountFrequency (%)
5811
< 0.1%
6021
< 0.1%
6118
< 0.1%
629
 
< 0.1%
6326
< 0.1%
ValueCountFrequency (%)
1838
 
< 0.1%
1797
 
< 0.1%
17622
 
< 0.1%
17522
 
< 0.1%
17459
0.1%

hepatomegaly
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing72799
Missing (%)90.0%
Memory size632.1 KiB
False
8082 
True
 
18
(Missing)
72799 
ValueCountFrequency (%)
False8082
 
10.0%
True18
 
< 0.1%
(Missing)72799
90.0%
2021-01-24T12:15:36.523759image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

icd_code
Categorical

HIGH CARDINALITY
MISSING

Distinct124
Distinct (%)3.7%
Missing77537
Missing (%)95.8%
Memory size632.1 KiB
A91A
1308 
A91B
325 
A91.A
220 
B34
181 
J02
149 
Other values (119)
1179 

Length

Max length13
Median length4
Mean length3.984830458
Min length3

Characters and Unicode

Total characters13397
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)0.7%

Sample

1st rowA913
2nd rowJ02
3rd rowJ02
4th rowJ02
5th rowA912
ValueCountFrequency (%)
A91A1308
 
1.6%
A91B325
 
0.4%
A91.A220
 
0.3%
B34181
 
0.2%
J02149
 
0.2%
A91C124
 
0.2%
B349124
 
0.2%
A912103
 
0.1%
A91C164
 
0.1%
J0645
 
0.1%
Other values (114)719
 
0.9%
(Missing)77537
95.8%
2021-01-24T12:15:36.728227image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a91a1308
38.9%
a91b325
 
9.7%
a91.a220
 
6.5%
b34181
 
5.4%
j02149
 
4.4%
b349124
 
3.7%
a91c124
 
3.7%
a912103
 
3.1%
a91c165
 
1.9%
j0645
 
1.3%
Other values (115)720
21.4%

Most occurring characters

ValueCountFrequency (%)
A3964
29.6%
92759
20.6%
12596
19.4%
B764
 
5.7%
0575
 
4.3%
3481
 
3.6%
.437
 
3.3%
J431
 
3.2%
4427
 
3.2%
2424
 
3.2%
Other values (16)539
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7482
55.8%
Uppercase Letter5475
40.9%
Other Punctuation438
 
3.3%
Space Separator2
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
A3964
72.4%
B764
 
14.0%
J431
 
7.9%
C230
 
4.2%
K22
 
0.4%
D16
 
0.3%
G15
 
0.3%
N12
 
0.2%
L10
 
0.2%
R4
 
0.1%
Other values (3)7
 
0.1%
ValueCountFrequency (%)
92759
36.9%
12596
34.7%
0575
 
7.7%
3481
 
6.4%
4427
 
5.7%
2424
 
5.7%
883
 
1.1%
667
 
0.9%
542
 
0.6%
728
 
0.4%
ValueCountFrequency (%)
.437
99.8%
,1
 
0.2%
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common7922
59.1%
Latin5475
40.9%

Most frequent character per script

ValueCountFrequency (%)
A3964
72.4%
B764
 
14.0%
J431
 
7.9%
C230
 
4.2%
K22
 
0.4%
D16
 
0.3%
G15
 
0.3%
N12
 
0.2%
L10
 
0.2%
R4
 
0.1%
Other values (3)7
 
0.1%
ValueCountFrequency (%)
92759
34.8%
12596
32.8%
0575
 
7.3%
3481
 
6.1%
.437
 
5.5%
4427
 
5.4%
2424
 
5.4%
883
 
1.0%
667
 
0.8%
542
 
0.5%
Other values (3)31
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII13397
100.0%

Most frequent character per block

ValueCountFrequency (%)
A3964
29.6%
92759
20.6%
12596
19.4%
B764
 
5.7%
0575
 
4.3%
3481
 
3.6%
.437
 
3.3%
J431
 
3.2%
4427
 
3.2%
2424
 
3.2%
Other values (16)539
 
4.0%

igg
Real number (ℝ)

MISSING

Distinct1778
Distinct (%)43.8%
Missing76839
Missing (%)95.0%
Infinite0
Infinite (%)0.0%
Mean9.909554964
Minimum-2.6
Maximum149.31
Zeros9
Zeros (%)< 0.1%
Memory size632.1 KiB
2021-01-24T12:15:36.830578image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-2.6
5-th percentile-0.08
Q10.74
median2.19
Q38.8125
95-th percentile54.9815
Maximum149.31
Range151.91
Interquartile range (IQR)8.0725

Descriptive statistics

Standard deviation18.46694539
Coefficient of variation (CV)1.863549418
Kurtosis10.17237838
Mean9.909554964
Median Absolute Deviation (MAD)1.924090909
Skewness2.986466676
Sum40232.79315
Variance341.0280719
MonotocityNot monotonic
2021-01-24T12:15:36.959326image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1819
 
< 0.1%
0.4918
 
< 0.1%
1.0817
 
< 0.1%
0.9917
 
< 0.1%
0.116
 
< 0.1%
0.4415
 
< 0.1%
0.6115
 
< 0.1%
0.3815
 
< 0.1%
0.5115
 
< 0.1%
0.5815
 
< 0.1%
Other values (1768)3898
 
4.8%
(Missing)76839
95.0%
ValueCountFrequency (%)
-2.61
< 0.1%
-2.421
< 0.1%
-2.261
< 0.1%
-2.231
< 0.1%
-2.171
< 0.1%
ValueCountFrequency (%)
149.311
< 0.1%
142.11
< 0.1%
130.781
< 0.1%
129.751
< 0.1%
124.781
< 0.1%

igg_interpretation
Categorical

MISSING

Distinct3
Distinct (%)0.1%
Missing76839
Missing (%)95.0%
Memory size632.1 KiB
Negative
3035 
Positive
931 
Equivocal
 
94

Length

Max length9
Median length8
Mean length8.023152709
Min length8

Characters and Unicode

Total characters32574
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNegative
2nd rowPositive
3rd rowNegative
4th rowNegative
5th rowNegative
ValueCountFrequency (%)
Negative3035
 
3.8%
Positive931
 
1.2%
Equivocal94
 
0.1%
(Missing)76839
95.0%
2021-01-24T12:15:37.181183image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T12:15:37.245095image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
negative3035
74.8%
positive931
 
22.9%
equivocal94
 
2.3%

Most occurring characters

ValueCountFrequency (%)
e7001
21.5%
i4991
15.3%
v4060
12.5%
t3966
12.2%
a3129
9.6%
N3035
9.3%
g3035
9.3%
o1025
 
3.1%
P931
 
2.9%
s931
 
2.9%
Other values (5)470
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter28514
87.5%
Uppercase Letter4060
 
12.5%

Most frequent character per category

ValueCountFrequency (%)
e7001
24.6%
i4991
17.5%
v4060
14.2%
t3966
13.9%
a3129
11.0%
g3035
10.6%
o1025
 
3.6%
s931
 
3.3%
q94
 
0.3%
u94
 
0.3%
Other values (2)188
 
0.7%
ValueCountFrequency (%)
N3035
74.8%
P931
 
22.9%
E94
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Latin32574
100.0%

Most frequent character per script

ValueCountFrequency (%)
e7001
21.5%
i4991
15.3%
v4060
12.5%
t3966
12.2%
a3129
9.6%
N3035
9.3%
g3035
9.3%
o1025
 
3.1%
P931
 
2.9%
s931
 
2.9%
Other values (5)470
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII32574
100.0%

Most frequent character per block

ValueCountFrequency (%)
e7001
21.5%
i4991
15.3%
v4060
12.5%
t3966
12.2%
a3129
9.6%
N3035
9.3%
g3035
9.3%
o1025
 
3.1%
P931
 
2.9%
s931
 
2.9%
Other values (5)470
 
1.4%

igm
Real number (ℝ)

MISSING

Distinct2003
Distinct (%)49.3%
Missing76839
Missing (%)95.0%
Infinite0
Infinite (%)0.0%
Mean11.13066564
Minimum-3.66
Maximum400.45
Zeros1
Zeros (%)< 0.1%
Memory size632.1 KiB
2021-01-24T12:15:37.338599image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-3.66
5-th percentile0.19
Q11.68
median3.52
Q312.6425
95-th percentile47.965
Maximum400.45
Range404.11
Interquartile range (IQR)10.9625

Descriptive statistics

Standard deviation21.89807962
Coefficient of variation (CV)1.967364786
Kurtosis94.09691277
Mean11.13066564
Median Absolute Deviation (MAD)2.45
Skewness7.43915377
Sum45190.50248
Variance479.525891
MonotocityNot monotonic
2021-01-24T12:15:37.459797image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5517
 
< 0.1%
1.5717
 
< 0.1%
1.6315
 
< 0.1%
1.5115
 
< 0.1%
1.8514
 
< 0.1%
1.6613
 
< 0.1%
212
 
< 0.1%
1.6112
 
< 0.1%
1.6812
 
< 0.1%
1.0112
 
< 0.1%
Other values (1993)3921
 
4.8%
(Missing)76839
95.0%
ValueCountFrequency (%)
-3.661
< 0.1%
-3.581
< 0.1%
-3.561
< 0.1%
-3.441
< 0.1%
-3.381
< 0.1%
ValueCountFrequency (%)
400.451
< 0.1%
350.261
< 0.1%
341.481
< 0.1%
328.711
< 0.1%
313.351
< 0.1%

igm_interpretation
Categorical

MISSING

Distinct3
Distinct (%)0.1%
Missing76839
Missing (%)95.0%
Memory size632.1 KiB
Negative
2841 
Positive
1084 
Equivocal
 
135

Length

Max length9
Median length8
Mean length8.033251232
Min length8

Characters and Unicode

Total characters32615
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNegative
2nd rowPositive
3rd rowNegative
4th rowNegative
5th rowNegative
ValueCountFrequency (%)
Negative2841
 
3.5%
Positive1084
 
1.3%
Equivocal135
 
0.2%
(Missing)76839
95.0%
2021-01-24T12:15:37.656800image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T12:15:37.717589image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
negative2841
70.0%
positive1084
 
26.7%
equivocal135
 
3.3%

Most occurring characters

ValueCountFrequency (%)
e6766
20.7%
i5144
15.8%
v4060
12.4%
t3925
12.0%
a2976
9.1%
N2841
8.7%
g2841
8.7%
o1219
 
3.7%
P1084
 
3.3%
s1084
 
3.3%
Other values (5)675
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter28555
87.6%
Uppercase Letter4060
 
12.4%

Most frequent character per category

ValueCountFrequency (%)
e6766
23.7%
i5144
18.0%
v4060
14.2%
t3925
13.7%
a2976
10.4%
g2841
9.9%
o1219
 
4.3%
s1084
 
3.8%
q135
 
0.5%
u135
 
0.5%
Other values (2)270
 
0.9%
ValueCountFrequency (%)
N2841
70.0%
P1084
 
26.7%
E135
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Latin32615
100.0%

Most frequent character per script

ValueCountFrequency (%)
e6766
20.7%
i5144
15.8%
v4060
12.4%
t3925
12.0%
a2976
9.1%
N2841
8.7%
g2841
8.7%
o1219
 
3.7%
P1084
 
3.3%
s1084
 
3.3%
Other values (5)675
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII32615
100.0%

Most frequent character per block

ValueCountFrequency (%)
e6766
20.7%
i5144
15.8%
v4060
12.4%
t3925
12.0%
a2976
9.1%
N2841
8.7%
g2841
8.7%
o1219
 
3.7%
P1084
 
3.3%
s1084
 
3.3%
Other values (5)675
 
2.1%

impairment
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)50.0%
Missing80897
Missing (%)> 99.9%
Memory size632.1 KiB
True
 
2
(Missing)
80897 
ValueCountFrequency (%)
True2
 
< 0.1%
(Missing)80897
> 99.9%
2021-01-24T12:15:37.757472image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

jaundice
Boolean

MISSING

Distinct2
Distinct (%)14.3%
Missing80885
Missing (%)> 99.9%
Memory size632.1 KiB
False
 
12
True
 
2
(Missing)
80885 
ValueCountFrequency (%)
False12
 
< 0.1%
True2
 
< 0.1%
(Missing)80885
> 99.9%
2021-01-24T12:15:37.789476image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

lathargy_severe
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing53364
Missing (%)66.0%
Memory size632.1 KiB
False
26891 
True
 
644
(Missing)
53364 
ValueCountFrequency (%)
False26891
33.2%
True644
 
0.8%
(Missing)53364
66.0%
2021-01-24T12:15:37.823742image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

liver_acute
Boolean

MISSING

Distinct2
Distinct (%)13.3%
Missing80884
Missing (%)> 99.9%
Memory size632.1 KiB
True
 
10
False
 
5
(Missing)
80884 
ValueCountFrequency (%)
True10
 
< 0.1%
False5
 
< 0.1%
(Missing)80884
> 99.9%
2021-01-24T12:15:37.860318image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

liver_involved
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)9.1%
Missing80888
Missing (%)> 99.9%
Memory size632.1 KiB
True
 
11
(Missing)
80888 
ValueCountFrequency (%)
True11
 
< 0.1%
(Missing)80888
> 99.9%
2021-01-24T12:15:37.892811image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

lymphocytes
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1089
Distinct (%)13.5%
Missing72803
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean2.172581176
Minimum0.016
Maximum12.6
Zeros0
Zeros (%)0.0%
Memory size632.1 KiB
2021-01-24T12:15:37.962736image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.016
5-th percentile0.6
Q11.1
median1.79
Q32.8
95-th percentile5.09
Maximum12.6
Range12.584
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation1.489704661
Coefficient of variation (CV)0.6856842345
Kurtosis4.635377087
Mean2.172581176
Median Absolute Deviation (MAD)0.79
Skewness1.751792601
Sum17589.2172
Variance2.219219976
MonotocityNot monotonic
2021-01-24T12:15:38.091998image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2138
 
0.2%
1.3136
 
0.2%
1126
 
0.2%
1.7125
 
0.2%
0.9124
 
0.2%
1.4122
 
0.2%
1.1118
 
0.1%
1.5118
 
0.1%
1.8116
 
0.1%
0.8106
 
0.1%
Other values (1079)6867
 
8.5%
(Missing)72803
90.0%
ValueCountFrequency (%)
0.0161
< 0.1%
0.041
< 0.1%
0.0482
< 0.1%
0.0531
< 0.1%
0.0581
< 0.1%
ValueCountFrequency (%)
12.61
< 0.1%
12.461
< 0.1%
12.161
< 0.1%
11.81
< 0.1%
11.121
< 0.1%

lymphocytes_percent
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct956
Distinct (%)11.8%
Missing72827
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean27.03719066
Minimum0.234
Maximum90.2
Zeros0
Zeros (%)0.0%
Memory size632.1 KiB
2021-01-24T12:15:38.211478image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.234
5-th percentile8.2
Q116
median24.5
Q335.7
95-th percentile54.6
Maximum90.2
Range89.966
Interquartile range (IQR)19.7

Descriptive statistics

Standard deviation14.40317403
Coefficient of variation (CV)0.5327171086
Kurtosis0.3687916755
Mean27.03719066
Median Absolute Deviation (MAD)9.4
Skewness0.8148987963
Sum218244.203
Variance207.4514222
MonotocityNot monotonic
2021-01-24T12:15:38.325662image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.437
 
< 0.1%
21.837
 
< 0.1%
21.435
 
< 0.1%
18.734
 
< 0.1%
19.934
 
< 0.1%
1832
 
< 0.1%
22.532
 
< 0.1%
20.632
 
< 0.1%
1631
 
< 0.1%
14.531
 
< 0.1%
Other values (946)7737
 
9.6%
(Missing)72827
90.0%
ValueCountFrequency (%)
0.2341
< 0.1%
0.2471
< 0.1%
0.6171
< 0.1%
0.7831
< 0.1%
0.8711
< 0.1%
ValueCountFrequency (%)
90.21
< 0.1%
86.91
< 0.1%
84.21
< 0.1%
83.21
< 0.1%
81.51
< 0.1%

monocytes
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED

Distinct949
Distinct (%)11.7%
Missing72803
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean0.8163214427
Minimum0.004
Maximum374
Zeros0
Zeros (%)0.0%
Memory size632.1 KiB
2021-01-24T12:15:38.439309image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.004
5-th percentile0.2
Q10.41575
median0.66
Q31
95-th percentile1.6725
Maximum374
Range373.996
Interquartile range (IQR)0.58425

Descriptive statistics

Standard deviation4.178178711
Coefficient of variation (CV)5.118300822
Kurtosis7864.696982
Mean0.8163214427
Median Absolute Deviation (MAD)0.26
Skewness88.04653391
Sum6608.9384
Variance17.45717734
MonotocityNot monotonic
2021-01-24T12:15:38.567312image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5332
 
0.4%
0.4324
 
0.4%
0.3296
 
0.4%
0.6283
 
0.3%
0.7274
 
0.3%
0.8222
 
0.3%
0.9188
 
0.2%
0.2156
 
0.2%
1143
 
0.2%
1.1127
 
0.2%
Other values (939)5751
 
7.1%
(Missing)72803
90.0%
ValueCountFrequency (%)
0.0041
< 0.1%
0.0061
< 0.1%
0.0071
< 0.1%
0.0081
< 0.1%
0.012
< 0.1%
ValueCountFrequency (%)
3741
< 0.1%
5.81
< 0.1%
5.71
< 0.1%
51
< 0.1%
4.71
< 0.1%

monocytes_percent
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct789
Distinct (%)9.7%
Missing72803
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean9.208251112
Minimum0.076
Maximum34.9
Zeros0
Zeros (%)0.0%
Memory size632.1 KiB
2021-01-24T12:15:38.690834image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.076
5-th percentile4
Q16.5
median8.54
Q311.2
95-th percentile16.5
Maximum34.9
Range34.824
Interquartile range (IQR)4.7

Descriptive statistics

Standard deviation4.000307019
Coefficient of variation (CV)0.4344263607
Kurtosis2.5578977
Mean9.208251112
Median Absolute Deviation (MAD)2.26
Skewness1.113847843
Sum74550.001
Variance16.00245624
MonotocityNot monotonic
2021-01-24T12:15:38.817607image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.798
 
0.1%
7.596
 
0.1%
8.193
 
0.1%
790
 
0.1%
8.390
 
0.1%
7.489
 
0.1%
6.389
 
0.1%
7.987
 
0.1%
6.987
 
0.1%
6.587
 
0.1%
Other values (779)7190
 
8.9%
(Missing)72803
90.0%
ValueCountFrequency (%)
0.0761
< 0.1%
0.1061
< 0.1%
0.1111
< 0.1%
0.1121
< 0.1%
0.1241
< 0.1%
ValueCountFrequency (%)
34.91
< 0.1%
34.11
< 0.1%
33.51
< 0.1%
32.11
< 0.1%
31.91
< 0.1%

movement
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing75299
Missing (%)93.1%
Memory size632.1 KiB
False
 
3385
True
 
2215
(Missing)
75299 
ValueCountFrequency (%)
False3385
 
4.2%
True2215
 
2.7%
(Missing)75299
93.1%
2021-01-24T12:15:38.895738image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

nasal_packing
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing79334
Missing (%)98.1%
Memory size632.1 KiB
False
 
1563
True
 
2
(Missing)
79334 
ValueCountFrequency (%)
False1563
 
1.9%
True2
 
< 0.1%
(Missing)79334
98.1%
2021-01-24T12:15:38.933119image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

neutrophils
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1368
Distinct (%)16.9%
Missing72804
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean5.729247251
Minimum0.144
Maximum59
Zeros0
Zeros (%)0.0%
Memory size632.1 KiB
2021-01-24T12:15:39.013123image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.144
5-th percentile1.4
Q12.87
median4.75
Q37.57
95-th percentile13.2
Maximum59
Range58.856
Interquartile range (IQR)4.7

Descriptive statistics

Standard deviation3.975540053
Coefficient of variation (CV)0.6939026854
Kurtosis8.321089458
Mean5.729247251
Median Absolute Deviation (MAD)2.17
Skewness1.902344851
Sum46378.2565
Variance15.80491871
MonotocityNot monotonic
2021-01-24T12:15:39.143524image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.157
 
0.1%
2.757
 
0.1%
2.456
 
0.1%
3.253
 
0.1%
3.352
 
0.1%
2.551
 
0.1%
4.348
 
0.1%
3.548
 
0.1%
2.247
 
0.1%
2.647
 
0.1%
Other values (1358)7579
 
9.4%
(Missing)72804
90.0%
ValueCountFrequency (%)
0.1441
< 0.1%
0.271
< 0.1%
0.351
< 0.1%
0.3791
< 0.1%
0.461
< 0.1%
ValueCountFrequency (%)
591
< 0.1%
411
< 0.1%
38.431
< 0.1%
33.21
< 0.1%
31.11
< 0.1%

neutrophils_percent
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct761
Distinct (%)9.4%
Missing72827
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean62.51500496
Minimum5.6
Maximum94.3
Zeros0
Zeros (%)0.0%
Memory size632.1 KiB
2021-01-24T12:15:39.272754image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum5.6
5-th percentile33.3
Q152.8
median64.3
Q374.4
95-th percentile84.4
Maximum94.3
Range88.7
Interquartile range (IQR)21.6

Descriptive statistics

Standard deviation15.49205502
Coefficient of variation (CV)0.2478133855
Kurtosis-0.1142386743
Mean62.51500496
Median Absolute Deviation (MAD)10.7
Skewness-0.5666227476
Sum504621.12
Variance240.0037689
MonotocityNot monotonic
2021-01-24T12:15:39.396037image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75.333
 
< 0.1%
6931
 
< 0.1%
73.831
 
< 0.1%
63.830
 
< 0.1%
69.529
 
< 0.1%
74.729
 
< 0.1%
70.128
 
< 0.1%
64.628
 
< 0.1%
74.128
 
< 0.1%
77.728
 
< 0.1%
Other values (751)7777
 
9.6%
(Missing)72827
90.0%
ValueCountFrequency (%)
5.61
< 0.1%
9.21
< 0.1%
9.81
< 0.1%
10.31
< 0.1%
10.71
< 0.1%
ValueCountFrequency (%)
94.31
< 0.1%
93.71
< 0.1%
93.31
< 0.1%
93.21
< 0.1%
931
< 0.1%
Distinct3
Distinct (%)0.1%
Missing75015
Missing (%)92.7%
Memory size632.1 KiB
Negative
5565 
Positive
 
280
Equivocal
 
39

Length

Max length9
Median length8
Mean length8.006628144
Min length8

Characters and Unicode

Total characters47111
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPositive
2nd rowNegative
3rd rowNegative
4th rowNegative
5th rowNegative
ValueCountFrequency (%)
Negative5565
 
6.9%
Positive280
 
0.3%
Equivocal39
 
< 0.1%
(Missing)75015
92.7%
2021-01-24T12:15:39.597716image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T12:15:39.652977image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
negative5565
94.6%
positive280
 
4.8%
equivocal39
 
0.7%

Most occurring characters

ValueCountFrequency (%)
e11410
24.2%
i6164
13.1%
v5884
12.5%
t5845
12.4%
a5604
11.9%
N5565
11.8%
g5565
11.8%
o319
 
0.7%
P280
 
0.6%
s280
 
0.6%
Other values (5)195
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter41227
87.5%
Uppercase Letter5884
 
12.5%

Most frequent character per category

ValueCountFrequency (%)
e11410
27.7%
i6164
15.0%
v5884
14.3%
t5845
14.2%
a5604
13.6%
g5565
13.5%
o319
 
0.8%
s280
 
0.7%
q39
 
0.1%
u39
 
0.1%
Other values (2)78
 
0.2%
ValueCountFrequency (%)
N5565
94.6%
P280
 
4.8%
E39
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Latin47111
100.0%

Most frequent character per script

ValueCountFrequency (%)
e11410
24.2%
i6164
13.1%
v5884
12.5%
t5845
12.4%
a5604
11.9%
N5565
11.8%
g5565
11.8%
o319
 
0.7%
P280
 
0.6%
s280
 
0.6%
Other values (5)195
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII47111
100.0%

Most frequent character per block

ValueCountFrequency (%)
e11410
24.2%
i6164
13.1%
v5884
12.5%
t5845
12.4%
a5604
11.9%
N5565
11.8%
g5565
11.8%
o319
 
0.7%
P280
 
0.6%
s280
 
0.6%
Other values (5)195
 
0.4%

oedema_pulmonary
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)0.1%
Missing79334
Missing (%)98.1%
Memory size632.1 KiB
False
 
1565
(Missing)
79334 
ValueCountFrequency (%)
False1565
 
1.9%
(Missing)79334
98.1%
2021-01-24T12:15:39.694676image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

outcome
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing68422
Missing (%)84.6%
Memory size632.1 KiB
Full recovery
12312 
Self discharge
 
111
Died
 
31
Transferred
 
23

Length

Max length14
Median length13
Mean length12.98284844
Min length4

Characters and Unicode

Total characters161987
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFull recovery
2nd rowFull recovery
3rd rowFull recovery
4th rowFull recovery
5th rowFull recovery
ValueCountFrequency (%)
Full recovery12312
 
15.2%
Self discharge111
 
0.1%
Died31
 
< 0.1%
Transferred23
 
< 0.1%
(Missing)68422
84.6%
2021-01-24T12:15:39.860242image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T12:15:39.923600image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
full12312
49.4%
recovery12312
49.4%
self111
 
0.4%
discharge111
 
0.4%
died31
 
0.1%
transferred23
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e24923
15.4%
r24804
15.3%
l24735
15.3%
12423
7.7%
c12423
7.7%
F12312
7.6%
u12312
7.6%
o12312
7.6%
v12312
7.6%
y12312
7.6%
Other values (11)1119
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter137087
84.6%
Uppercase Letter12477
 
7.7%
Space Separator12423
 
7.7%

Most frequent character per category

ValueCountFrequency (%)
e24923
18.2%
r24804
18.1%
l24735
18.0%
c12423
9.1%
u12312
9.0%
o12312
9.0%
v12312
9.0%
y12312
9.0%
d165
 
0.1%
i142
 
0.1%
Other values (6)647
 
0.5%
ValueCountFrequency (%)
F12312
98.7%
S111
 
0.9%
D31
 
0.2%
T23
 
0.2%
ValueCountFrequency (%)
12423
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin149564
92.3%
Common12423
 
7.7%

Most frequent character per script

ValueCountFrequency (%)
e24923
16.7%
r24804
16.6%
l24735
16.5%
c12423
8.3%
F12312
8.2%
u12312
8.2%
o12312
8.2%
v12312
8.2%
y12312
8.2%
d165
 
0.1%
Other values (10)954
 
0.6%
ValueCountFrequency (%)
12423
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII161987
100.0%

Most frequent character per block

ValueCountFrequency (%)
e24923
15.4%
r24804
15.3%
l24735
15.3%
12423
7.7%
c12423
7.7%
F12312
7.6%
u12312
7.6%
o12312
7.6%
v12312
7.6%
y12312
7.6%
Other values (11)1119
 
0.7%

parental_fluid
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)0.5%
Missing80684
Missing (%)99.7%
Memory size632.1 KiB
True
 
215
(Missing)
80684 
ValueCountFrequency (%)
True215
 
0.3%
(Missing)80684
99.7%
2021-01-24T12:15:39.974175image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

pcr_dengue_interpretation
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size632.1 KiB
Lab-confirmed Dengue
80899 

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters1617980
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLab-confirmed Dengue
2nd rowLab-confirmed Dengue
3rd rowLab-confirmed Dengue
4th rowLab-confirmed Dengue
5th rowLab-confirmed Dengue
ValueCountFrequency (%)
Lab-confirmed Dengue80899
100.0%
2021-01-24T12:15:40.127896image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T12:15:40.182577image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
dengue80899
50.0%
lab-confirmed80899
50.0%

Most occurring characters

ValueCountFrequency (%)
e242697
15.0%
n161798
 
10.0%
L80899
 
5.0%
a80899
 
5.0%
b80899
 
5.0%
-80899
 
5.0%
c80899
 
5.0%
o80899
 
5.0%
f80899
 
5.0%
i80899
 
5.0%
Other values (7)566293
35.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1294384
80.0%
Uppercase Letter161798
 
10.0%
Dash Punctuation80899
 
5.0%
Space Separator80899
 
5.0%

Most frequent character per category

ValueCountFrequency (%)
e242697
18.8%
n161798
12.5%
a80899
 
6.2%
b80899
 
6.2%
c80899
 
6.2%
o80899
 
6.2%
f80899
 
6.2%
i80899
 
6.2%
r80899
 
6.2%
m80899
 
6.2%
Other values (3)242697
18.8%
ValueCountFrequency (%)
L80899
50.0%
D80899
50.0%
ValueCountFrequency (%)
-80899
100.0%
ValueCountFrequency (%)
80899
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1456182
90.0%
Common161798
 
10.0%

Most frequent character per script

ValueCountFrequency (%)
e242697
16.7%
n161798
 
11.1%
L80899
 
5.6%
a80899
 
5.6%
b80899
 
5.6%
c80899
 
5.6%
o80899
 
5.6%
f80899
 
5.6%
i80899
 
5.6%
r80899
 
5.6%
Other values (5)404495
27.8%
ValueCountFrequency (%)
-80899
50.0%
80899
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1617980
100.0%

Most frequent character per block

ValueCountFrequency (%)
e242697
15.0%
n161798
 
10.0%
L80899
 
5.0%
a80899
 
5.0%
b80899
 
5.0%
-80899
 
5.0%
c80899
 
5.0%
o80899
 
5.0%
f80899
 
5.0%
i80899
 
5.0%
Other values (7)566293
35.0%

pcr_dengue_load
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct1825
Distinct (%)22.5%
Missing72800
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean170839081.2
Minimum0
Maximum1.68452 × 1011
Zeros6032
Zeros (%)7.5%
Memory size632.1 KiB
2021-01-24T12:15:40.268363image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31830.357143
95-th percentile261904761.9
Maximum1.68452 × 1011
Range1.68452 × 1011
Interquartile range (IQR)1830.357143

Descriptive statistics

Standard deviation2808985386
Coefficient of variation (CV)16.44228806
Kurtosis2097.164447
Mean170839081.2
Median Absolute Deviation (MAD)0
Skewness41.74880205
Sum1.383625719 × 1012
Variance7.890398897 × 1018
MonotocityNot monotonic
2021-01-24T12:15:40.392643image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06032
 
7.5%
6250006
 
< 0.1%
6250000004
 
< 0.1%
75000004
 
< 0.1%
7619000004
 
< 0.1%
9702000003
 
< 0.1%
73210003
 
< 0.1%
970238095.23
 
< 0.1%
601200003
 
< 0.1%
125000003
 
< 0.1%
Other values (1815)2034
 
2.5%
(Missing)72800
90.0%
ValueCountFrequency (%)
06032
7.5%
67.861
 
< 0.1%
78.571428571
 
< 0.1%
85.1191
 
< 0.1%
89.881
 
< 0.1%
ValueCountFrequency (%)
1.68452 × 10111
< 0.1%
1.11905 × 10111
< 0.1%
9.345238095 × 10101
< 0.1%
6.30952381 × 10101
< 0.1%
4.101190476 × 10101
< 0.1%

pcr_dengue_serotype
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size632.1 KiB
<LOD
80899 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters323596
Distinct characters4
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<LOD
2nd row<LOD
3rd row<LOD
4th row<LOD
5th row<LOD
ValueCountFrequency (%)
<LOD80899
100.0%
2021-01-24T12:15:40.603971image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T12:15:40.663416image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
lod80899
100.0%

Most occurring characters

ValueCountFrequency (%)
<80899
25.0%
L80899
25.0%
O80899
25.0%
D80899
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter242697
75.0%
Math Symbol80899
 
25.0%

Most frequent character per category

ValueCountFrequency (%)
L80899
33.3%
O80899
33.3%
D80899
33.3%
ValueCountFrequency (%)
<80899
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin242697
75.0%
Common80899
 
25.0%

Most frequent character per script

ValueCountFrequency (%)
L80899
33.3%
O80899
33.3%
D80899
33.3%
ValueCountFrequency (%)
<80899
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII323596
100.0%

Most frequent character per block

ValueCountFrequency (%)
<80899
25.0%
L80899
25.0%
O80899
25.0%
D80899
25.0%

perfusion
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)0.9%
Missing80785
Missing (%)99.9%
Memory size632.1 KiB
True
 
114
(Missing)
80785 
ValueCountFrequency (%)
True114
 
0.1%
(Missing)80785
99.9%
2021-01-24T12:15:40.696995image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

platelet_min
Real number (ℝ≥0)

MISSING
SKEWED

Distinct347
Distinct (%)22.2%
Missing79336
Missing (%)98.1%
Infinite0
Infinite (%)0.0%
Mean166.6206014
Minimum7
Maximum50000
Zeros0
Zeros (%)0.0%
Memory size632.1 KiB
2021-01-24T12:15:40.771981image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile29
Q174.5
median111
Q3180.5
95-th percentile309
Maximum50000
Range49993
Interquartile range (IQR)106

Descriptive statistics

Standard deviation1264.306341
Coefficient of variation (CV)7.58793529
Kurtosis1548.163912
Mean166.6206014
Median Absolute Deviation (MAD)47
Skewness39.25354873
Sum260428
Variance1598470.525
MonotocityNot monotonic
2021-01-24T12:15:40.907031image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9418
 
< 0.1%
9517
 
< 0.1%
7917
 
< 0.1%
7815
 
< 0.1%
8115
 
< 0.1%
11015
 
< 0.1%
10414
 
< 0.1%
9014
 
< 0.1%
10014
 
< 0.1%
9614
 
< 0.1%
Other values (337)1410
 
1.7%
(Missing)79336
98.1%
ValueCountFrequency (%)
73
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
102
< 0.1%
111
 
< 0.1%
ValueCountFrequency (%)
500001
< 0.1%
4691
< 0.1%
4631
< 0.1%
4411
< 0.1%
4401
< 0.1%

platelet_no
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct14
Distinct (%)0.9%
Missing79334
Missing (%)98.1%
Infinite0
Infinite (%)0.0%
Mean2.516932907
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Memory size632.1 KiB
2021-01-24T12:15:41.009502image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum27
Range26
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.808406287
Coefficient of variation (CV)0.7184960241
Kurtosis31.4057091
Mean2.516932907
Median Absolute Deviation (MAD)1
Skewness3.533171087
Sum3939
Variance3.270333298
MonotocityNot monotonic
2021-01-24T12:15:41.104216image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1524
 
0.6%
2427
 
0.5%
3267
 
0.3%
4173
 
0.2%
593
 
0.1%
641
 
0.1%
721
 
< 0.1%
88
 
< 0.1%
93
 
< 0.1%
103
 
< 0.1%
Other values (4)5
 
< 0.1%
(Missing)79334
98.1%
ValueCountFrequency (%)
1524
0.6%
2427
0.5%
3267
0.3%
4173
 
0.2%
593
 
0.1%
ValueCountFrequency (%)
271
 
< 0.1%
211
 
< 0.1%
152
< 0.1%
111
 
< 0.1%
103
< 0.1%

platelets
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing79334
Missing (%)98.1%
Memory size632.1 KiB
False
 
1559
True
 
6
(Missing)
79334 
ValueCountFrequency (%)
False1559
 
1.9%
True6
 
< 0.1%
(Missing)79334
98.1%
2021-01-24T12:15:41.163974image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

pleural_effusion
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing79334
Missing (%)98.1%
Memory size632.1 KiB
False
 
1535
True
 
30
(Missing)
79334 
ValueCountFrequency (%)
False1535
 
1.9%
True30
 
< 0.1%
(Missing)79334
98.1%
2021-01-24T12:15:41.202819image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

plt
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct520
Distinct (%)6.4%
Missing72803
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean233.0960351
Minimum18
Maximum829
Zeros0
Zeros (%)0.0%
Memory size632.1 KiB
2021-01-24T12:15:41.285443image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile116
Q1181
median227
Q3278
95-th percentile369
Maximum829
Range811
Interquartile range (IQR)97

Descriptive statistics

Standard deviation78.58156097
Coefficient of variation (CV)0.337120968
Kurtosis2.026154663
Mean233.0960351
Median Absolute Deviation (MAD)48
Skewness0.7417786805
Sum1887145.5
Variance6175.061725
MonotocityNot monotonic
2021-01-24T12:15:41.413432image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22468
 
0.1%
23657
 
0.1%
23556
 
0.1%
25054
 
0.1%
20454
 
0.1%
23454
 
0.1%
19554
 
0.1%
21753
 
0.1%
20853
 
0.1%
22152
 
0.1%
Other values (510)7541
 
9.3%
(Missing)72803
90.0%
ValueCountFrequency (%)
181
 
< 0.1%
261
 
< 0.1%
271
 
< 0.1%
283
< 0.1%
292
< 0.1%
ValueCountFrequency (%)
8291
< 0.1%
7511
< 0.1%
7091
< 0.1%
6911
< 0.1%
6541
< 0.1%

pulse
Real number (ℝ≥0)

MISSING

Distinct30
Distinct (%)26.8%
Missing80787
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean114.5714286
Minimum80
Maximum165
Zeros0
Zeros (%)0.0%
Memory size632.1 KiB
2021-01-24T12:15:41.527040image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile96
Q1102
median117
Q3120
95-th percentile134.7
Maximum165
Range85
Interquartile range (IQR)18

Descriptive statistics

Standard deviation13.45577627
Coefficient of variation (CV)0.1174444313
Kurtosis0.9453342865
Mean114.5714286
Median Absolute Deviation (MAD)9
Skewness0.4083712557
Sum12832
Variance181.0579151
MonotocityNot monotonic
2021-01-24T12:15:41.626970image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
12031
 
< 0.1%
10019
 
< 0.1%
11014
 
< 0.1%
13011
 
< 0.1%
1263
 
< 0.1%
1063
 
< 0.1%
1082
 
< 0.1%
1022
 
< 0.1%
1152
 
< 0.1%
1142
 
< 0.1%
Other values (20)23
 
< 0.1%
(Missing)80787
99.9%
ValueCountFrequency (%)
801
< 0.1%
902
< 0.1%
921
< 0.1%
941
< 0.1%
962
< 0.1%
ValueCountFrequency (%)
1651
< 0.1%
1451
< 0.1%
1441
< 0.1%
1402
< 0.1%
1381
< 0.1%

pulse_status
Categorical

MISSING

Distinct3
Distinct (%)2.6%
Missing80785
Missing (%)99.9%
Memory size632.1 KiB
Weak
89 
Strong
21 
Not done / Not detected
 
4

Length

Max length23
Median length4
Mean length5.035087719
Min length4

Characters and Unicode

Total characters574
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWeak
2nd rowWeak
3rd rowWeak
4th rowWeak
5th rowWeak
ValueCountFrequency (%)
Weak89
 
0.1%
Strong21
 
< 0.1%
Not done / Not detected4
 
< 0.1%
(Missing)80785
99.9%
2021-01-24T12:15:41.813655image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T12:15:41.871110image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
weak89
68.5%
strong21
 
16.2%
not8
 
6.2%
4
 
3.1%
detected4
 
3.1%
done4
 
3.1%

Most occurring characters

ValueCountFrequency (%)
e105
18.3%
W89
15.5%
a89
15.5%
k89
15.5%
t37
 
6.4%
o33
 
5.7%
n25
 
4.4%
S21
 
3.7%
r21
 
3.7%
g21
 
3.7%
Other values (5)44
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter436
76.0%
Uppercase Letter118
 
20.6%
Space Separator16
 
2.8%
Other Punctuation4
 
0.7%

Most frequent character per category

ValueCountFrequency (%)
e105
24.1%
a89
20.4%
k89
20.4%
t37
 
8.5%
o33
 
7.6%
n25
 
5.7%
r21
 
4.8%
g21
 
4.8%
d12
 
2.8%
c4
 
0.9%
ValueCountFrequency (%)
W89
75.4%
S21
 
17.8%
N8
 
6.8%
ValueCountFrequency (%)
16
100.0%
ValueCountFrequency (%)
/4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin554
96.5%
Common20
 
3.5%

Most frequent character per script

ValueCountFrequency (%)
e105
19.0%
W89
16.1%
a89
16.1%
k89
16.1%
t37
 
6.7%
o33
 
6.0%
n25
 
4.5%
S21
 
3.8%
r21
 
3.8%
g21
 
3.8%
Other values (3)24
 
4.3%
ValueCountFrequency (%)
16
80.0%
/4
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII574
100.0%

Most frequent character per block

ValueCountFrequency (%)
e105
18.3%
W89
15.5%
a89
15.5%
k89
15.5%
t37
 
6.4%
o33
 
5.7%
n25
 
4.4%
S21
 
3.7%
r21
 
3.7%
g21
 
3.7%
Other values (5)44
7.7%

rbc
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing79334
Missing (%)98.1%
Memory size632.1 KiB
False
 
1558
True
 
7
(Missing)
79334 
ValueCountFrequency (%)
False1558
 
1.9%
True7
 
< 0.1%
(Missing)79334
98.1%
2021-01-24T12:15:42.710804image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

respiratory_distress
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)3.0%
Missing80866
Missing (%)> 99.9%
Memory size632.1 KiB
True
 
33
(Missing)
80866 
ValueCountFrequency (%)
True33
 
< 0.1%
(Missing)80866
> 99.9%
2021-01-24T12:15:42.756439image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

restlessness
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing53364
Missing (%)66.0%
Memory size632.1 KiB
False
27529 
True
 
6
(Missing)
53364 
ValueCountFrequency (%)
False27529
34.0%
True6
 
< 0.1%
(Missing)53364
66.0%
2021-01-24T12:15:42.791163image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

sbp
Real number (ℝ≥0)

MISSING

Distinct11
Distinct (%)10.0%
Missing80789
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean93.72727273
Minimum60
Maximum120
Zeros0
Zeros (%)0.0%
Memory size632.1 KiB
2021-01-24T12:15:42.846981image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile80
Q190
median95
Q3100
95-th percentile110
Maximum120
Range60
Interquartile range (IQR)10

Descriptive statistics

Standard deviation10.23656792
Coefficient of variation (CV)0.1092165345
Kurtosis0.456242554
Mean93.72727273
Median Absolute Deviation (MAD)5
Skewness-0.3346212546
Sum10310
Variance104.7873228
MonotocityNot monotonic
2021-01-24T12:15:42.930971image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
10032
 
< 0.1%
9032
 
< 0.1%
8013
 
< 0.1%
11011
 
< 0.1%
959
 
< 0.1%
854
 
< 0.1%
1053
 
< 0.1%
752
 
< 0.1%
702
 
< 0.1%
1201
 
< 0.1%
(Missing)80789
99.9%
ValueCountFrequency (%)
601
 
< 0.1%
702
 
< 0.1%
752
 
< 0.1%
8013
< 0.1%
854
 
< 0.1%
ValueCountFrequency (%)
1201
 
< 0.1%
11011
 
< 0.1%
1053
 
< 0.1%
10032
< 0.1%
959
 
< 0.1%

serology_interpretation
Categorical

MISSING

Distinct3
Distinct (%)0.1%
Missing78859
Missing (%)97.5%
Memory size632.1 KiB
Inconclusive
992 
Probable Secondary
747 
Probable primary
301 

Length

Max length18
Median length16
Mean length14.7872549
Min length12

Characters and Unicode

Total characters30166
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowProbable Secondary
2nd rowInconclusive
3rd rowInconclusive
4th rowInconclusive
5th rowInconclusive
ValueCountFrequency (%)
Inconclusive992
 
1.2%
Probable Secondary747
 
0.9%
Probable primary301
 
0.4%
(Missing)78859
97.5%
2021-01-24T12:15:43.120950image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T12:15:43.176060image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
probable1048
33.9%
inconclusive992
32.1%
secondary747
24.2%
primary301
 
9.7%

Most occurring characters

ValueCountFrequency (%)
o2787
 
9.2%
e2787
 
9.2%
c2731
 
9.1%
n2731
 
9.1%
r2397
 
7.9%
b2096
 
6.9%
a2096
 
6.9%
l2040
 
6.8%
i1293
 
4.3%
P1048
 
3.5%
Other values (10)8160
27.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter26331
87.3%
Uppercase Letter2787
 
9.2%
Space Separator1048
 
3.5%

Most frequent character per category

ValueCountFrequency (%)
o2787
10.6%
e2787
10.6%
c2731
10.4%
n2731
10.4%
r2397
9.1%
b2096
8.0%
a2096
8.0%
l2040
7.7%
i1293
 
4.9%
y1048
 
4.0%
Other values (6)4325
16.4%
ValueCountFrequency (%)
P1048
37.6%
I992
35.6%
S747
26.8%
ValueCountFrequency (%)
1048
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin29118
96.5%
Common1048
 
3.5%

Most frequent character per script

ValueCountFrequency (%)
o2787
 
9.6%
e2787
 
9.6%
c2731
 
9.4%
n2731
 
9.4%
r2397
 
8.2%
b2096
 
7.2%
a2096
 
7.2%
l2040
 
7.0%
i1293
 
4.4%
P1048
 
3.6%
Other values (9)7112
24.4%
ValueCountFrequency (%)
1048
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII30166
100.0%

Most frequent character per block

ValueCountFrequency (%)
o2787
 
9.2%
e2787
 
9.2%
c2731
 
9.1%
n2731
 
9.1%
r2397
 
7.9%
b2096
 
6.9%
a2096
 
6.9%
l2040
 
6.8%
i1293
 
4.3%
P1048
 
3.5%
Other values (10)8160
27.1%

shock
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.1 KiB
False
80785 
True
 
114
ValueCountFrequency (%)
False80785
99.9%
True114
 
0.1%
2021-01-24T12:15:43.222972image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

shock_multiple
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.1 KiB
False
80899 
ValueCountFrequency (%)
False80899
100.0%
2021-01-24T12:15:43.255273image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

skin_clammy
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing53364
Missing (%)66.0%
Memory size632.1 KiB
False
27483 
True
 
52
(Missing)
53364 
ValueCountFrequency (%)
False27483
34.0%
True52
 
0.1%
(Missing)53364
66.0%
2021-01-24T12:15:43.285304image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

skin_flush
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing72799
Missing (%)90.0%
Memory size632.1 KiB
False
 
6735
True
 
1365
(Missing)
72799 
ValueCountFrequency (%)
False6735
 
8.3%
True1365
 
1.7%
(Missing)72799
90.0%
2021-01-24T12:15:43.319889image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

skin_rash
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing72799
Missing (%)90.0%
Memory size632.1 KiB
False
7930 
True
 
170
(Missing)
72799 
ValueCountFrequency (%)
False7930
 
9.8%
True170
 
0.2%
(Missing)72799
90.0%
2021-01-24T12:15:43.354123image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

vomiting
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing43727
Missing (%)54.1%
Memory size632.1 KiB
False
33762 
True
 
3410
(Missing)
43727 
ValueCountFrequency (%)
False33762
41.7%
True3410
 
4.2%
(Missing)43727
54.1%
2021-01-24T12:15:43.386922image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

wbc
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1594
Distinct (%)19.7%
Missing72803
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean8.76609585
Minimum0.85
Maximum49.58
Zeros0
Zeros (%)0.0%
Memory size632.1 KiB
2021-01-24T12:15:43.460221image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.85
5-th percentile3.09
Q15.2
median7.71
Q311.2
95-th percentile18.1025
Maximum49.58
Range48.73
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.832291864
Coefficient of variation (CV)0.5512478926
Kurtosis3.693048886
Mean8.76609585
Median Absolute Deviation (MAD)2.835
Skewness1.434827463
Sum70970.312
Variance23.35104465
MonotocityNot monotonic
2021-01-24T12:15:43.585281image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.450
 
0.1%
548
 
0.1%
4.346
 
0.1%
4.744
 
0.1%
8.442
 
0.1%
3.840
 
< 0.1%
7.239
 
< 0.1%
5.738
 
< 0.1%
5.438
 
< 0.1%
5.238
 
< 0.1%
Other values (1584)7673
 
9.5%
(Missing)72803
90.0%
ValueCountFrequency (%)
0.851
 
< 0.1%
0.91
 
< 0.1%
1.153
< 0.1%
1.231
 
< 0.1%
1.251
 
< 0.1%
ValueCountFrequency (%)
49.581
< 0.1%
45.41
< 0.1%
44.71
< 0.1%
401
< 0.1%
39.961
< 0.1%

weight
Real number (ℝ≥0)

Distinct279
Distinct (%)0.3%
Missing7
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean24.30571256
Minimum7.2
Maximum91
Zeros0
Zeros (%)0.0%
Memory size632.1 KiB
2021-01-24T12:15:43.702679image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum7.2
5-th percentile10.5
Q115
median21
Q330
95-th percentile48
Maximum91
Range83.8
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.77308812
Coefficient of variation (CV)0.484375354
Kurtosis1.302602711
Mean24.30571256
Median Absolute Deviation (MAD)7
Skewness1.165799531
Sum1966137.7
Variance138.605604
MonotocityNot monotonic
2021-01-24T12:15:43.820258image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
204070
 
5.0%
153659
 
4.5%
182726
 
3.4%
132692
 
3.3%
252670
 
3.3%
142646
 
3.3%
172528
 
3.1%
222472
 
3.1%
192436
 
3.0%
302413
 
3.0%
Other values (269)52580
65.0%
ValueCountFrequency (%)
7.211
< 0.1%
7.36
 
< 0.1%
7.520
< 0.1%
7.827
< 0.1%
7.99
 
< 0.1%
ValueCountFrequency (%)
915
 
< 0.1%
8414
< 0.1%
8034
< 0.1%
799
 
< 0.1%
789
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.1 KiB
False
80844 
True
 
55
ValueCountFrequency (%)
False80844
99.9%
True55
 
0.1%
2021-01-24T12:15:43.883726image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Interactions

2021-01-24T12:14:27.372149image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:27.475010image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:27.565432image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:27.662641image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:27.989652image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:28.082386image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:28.170547image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:28.254695image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:28.348163image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:28.431881image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:28.517737image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:28.606699image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:28.693156image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:28.774418image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:28.860651image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:28.947794image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:29.030356image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:29.108781image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:29.201533image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:29.286622image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:29.370398image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:29.459977image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:29.530213image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:29.602785image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:29.697288image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:29.778012image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:29.865735image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:29.952828image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:30.042764image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:30.101819image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:30.184868image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:30.245983image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:30.312553image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:30.398828image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:30.575599image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:30.675338image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:30.764598image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:30.852799image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:30.939962image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:31.029846image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:31.118762image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:31.208897image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:31.297618image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:31.360270image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:31.422014image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:31.497416image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:31.589135image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:31.649014image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:31.710870image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:31.792655image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:31.876875image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:31.960914image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:32.050900image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:32.139445image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:32.203004image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:32.286260image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:32.344049image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:32.415495image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:32.499839image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:32.585670image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:32.669246image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:32.755380image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:32.842624image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:32.926994image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:33.015325image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:33.101835image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:33.185340image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:33.266972image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:33.326620image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:33.385371image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:33.463782image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:33.668373image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:33.741523image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:33.800242image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:33.878536image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:33.957558image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:34.045221image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:34.135470image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:34.224754image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:34.284694image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:34.368891image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:34.427601image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:34.490609image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:34.578458image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:34.667904image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:34.763531image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:34.852388image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:34.946251image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:35.047923image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:35.154194image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:35.261158image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:35.354561image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:35.469279image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:35.550365image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:35.614961image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:35.693159image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:35.787642image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:35.849336image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:35.912921image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:35.995891image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:36.081377image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:36.165842image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:36.228311image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:36.286246image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:36.344408image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:36.403177image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:36.463390image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:36.522698image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:36.584679image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:36.668081image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:36.726200image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:36.783206image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:36.840651image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:36.900134image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:36.974007image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:37.042159image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:37.101399image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:37.162205image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:37.396261image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:37.471773image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:37.530375image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:37.587954image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:37.646691image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:37.704698image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:37.769671image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:37.851358image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:37.937055image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:38.023590image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:38.103087image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:38.186114image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:38.247267image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:38.305005image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:38.368376image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:38.449227image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:38.527884image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:38.612390image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:38.700826image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:38.779596image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:38.852834image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:38.933202image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:39.015089image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:39.095395image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:39.170286image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:39.231512image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:39.289738image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:39.358898image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:39.439606image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:39.495736image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:39.552391image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:39.626232image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:39.699480image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:39.789989image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:39.847880image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:39.905679image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:39.963687image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:40.022444image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:40.080463image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:40.148532image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:40.206503image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:40.296631image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:40.364191image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:40.434978image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:40.493452image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:40.552149image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:40.609994image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:40.668875image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:40.727108image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:40.787623image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:40.845224image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:40.937897image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:41.007961image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:41.064439image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:41.135277image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:41.209418image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:41.271914image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:41.354035image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:41.434939image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:41.681993image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:41.769571image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:41.836080image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:41.896642image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:41.959715image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:42.028697image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:42.101937image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:42.189317image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:42.258467image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:42.328206image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:42.401810image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:42.477822image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:42.552735image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:42.628506image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:42.705968image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:42.786721image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:42.848975image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:42.921789image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:43.003745image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:43.079269image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:43.144369image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:43.213006image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:43.291175image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:43.368746image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:43.456194image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:43.544570image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:43.629266image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:43.715323image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:43.778145image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:43.856812image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:43.915870image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:43.986983image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:44.074302image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:44.154880image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:44.240443image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:44.325414image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:44.404263image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:44.489950image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:44.576038image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:44.661509image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:44.739710image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:44.799554image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:44.858197image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:44.929205image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:45.015991image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:45.073016image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:45.130219image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:45.211526image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:45.302844image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:45.388737image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:45.475319image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:45.559423image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:45.648209image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:45.737579image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:45.820245image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:45.905206image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:45.987336image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:46.071237image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:46.154497image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:46.244049image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:46.330289image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:46.410686image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:46.496316image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:46.579989image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:46.673614image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:46.756380image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:46.846636image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:46.932728image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:47.053030image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:47.172330image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:47.470192image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:47.562014image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:47.654508image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:47.732130image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:47.820650image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:47.900709image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:47.979951image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:48.070455image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:48.135762image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:48.222496image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:48.294645image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:48.359352image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:48.446438image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:48.545613image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:48.640201image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:48.718331image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:48.797893image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:48.919640image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:49.014985image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:49.112695image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:49.194287image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:49.260078image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:49.338230image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:49.423584image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:49.523496image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:49.595856image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:49.666043image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:49.751112image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:49.837208image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:49.933074image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:50.024278image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:50.118069image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:50.220944image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:50.281718image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:50.372861image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:50.446680image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:50.521434image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:50.614319image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:50.714442image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:50.812223image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:50.900395image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:50.987568image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:51.074569image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:51.162516image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:51.257510image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:51.336298image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:51.393887image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:51.460147image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:51.538993image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:51.627255image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:51.691191image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:51.754420image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:51.838522image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:51.923563image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:52.011337image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:52.093831image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:52.175847image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:52.265939image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:52.327735image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:52.411303image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:52.470880image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:52.543185image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:52.630347image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:52.712825image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:52.787909image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:52.871435image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:52.954347image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:53.039803image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:53.126136image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:53.215695image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:53.295334image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:53.354135image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:53.413673image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:53.492796image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:53.617156image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:53.682226image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:53.745704image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:53.831354image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:53.913579image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:53.998674image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:54.080209image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:54.159768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:54.247059image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:54.311355image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:54.392607image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:54.455516image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:54.534974image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:54.911834image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:55.011998image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:55.092418image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:55.172490image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:55.255083image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:55.338567image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:55.425228image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:55.507192image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:55.587734image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:55.652993image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:55.712677image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:55.791311image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:55.889559image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:55.955765image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:56.020343image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:56.099568image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:56.176244image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:56.267187image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:56.355632image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:56.443458image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:56.532789image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:56.593272image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:56.675908image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:56.733754image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:56.805574image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:56.894418image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:56.979657image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:57.063355image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:57.148952image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:57.241417image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:57.325332image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:57.414777image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:57.501121image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:57.581006image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:57.638399image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:57.696375image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:57.768412image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:57.856542image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:57.915031image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:57.973550image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:58.058071image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:58.138731image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:58.229257image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:58.317286image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:58.403547image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:58.492594image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:58.553857image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:58.636642image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:58.695496image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:58.764968image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:58.851976image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:58.941426image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:59.028638image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:59.117938image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:59.208307image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:59.300574image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:59.391745image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:59.479015image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:59.565247image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:59.628166image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:59.687651image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:59.758839image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:59.848762image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:59.912331image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:14:59.974403image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:00.063240image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:00.147434image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:00.237065image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:00.325829image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:00.413369image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:00.498586image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:00.557953image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:00.637695image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:00.696314image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:00.770151image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:00.855754image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:00.939283image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:01.026212image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:01.116147image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:01.200035image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:01.281657image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:01.367917image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:01.455655image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:01.539887image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:01.603193image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:01.664088image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:01.738727image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:01.825108image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:01.884936image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:01.947295image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:02.028855image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:02.109936image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:02.191376image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:02.270569image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:02.350645image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:02.431410image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:02.491760image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:02.565206image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:02.625283image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:02.704299image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:02.782127image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:02.860043image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:02.940027image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:03.020419image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:03.098434image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:03.171963image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:03.250368image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:03.330128image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:03.759152image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:03.835947image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:03.900065image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:03.980672image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:04.062491image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:04.123156image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:04.183032image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:04.260770image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:04.334085image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:04.425445image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:04.485444image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:04.544035image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:04.603578image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:04.689849image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:04.746025image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:04.807931image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:04.868064image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:04.931088image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:05.023011image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:05.090491image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:05.150884image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:05.210383image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:05.271013image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:05.333406image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:05.395551image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:05.455471image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:05.514832image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:05.574244image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:05.635154image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:05.695224image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:05.755816image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:05.815261image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:05.875741image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:05.960116image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:06.043124image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:06.100800image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:06.158467image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:06.215793image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:06.274008image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:06.337827image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:06.437512image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:06.504724image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:06.564695image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:06.647426image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:06.719762image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:06.788526image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:06.845569image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:06.934644image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:07.000282image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:07.061153image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:07.121528image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:07.183134image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:07.242144image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:07.314754image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:07.371555image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:07.430637image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:07.499011image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:07.563382image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:07.639862image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:07.726295image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:07.803151image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:07.884266image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:07.963949image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:08.023271image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:08.092863image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:08.167969image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:08.250037image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:08.334741image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:08.437588image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:08.515665image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:08.595306image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:08.667081image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:08.745106image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:08.818367image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:08.893382image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:08.971150image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:09.047874image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:09.108582image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:09.182541image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:09.255964image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:09.326784image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:09.401718image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:09.470809image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:09.548065image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:09.635344image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:09.723293image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:09.810399image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:09.900830image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:09.960749image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:10.048132image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:10.109668image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:10.180215image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:10.269273image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:10.358903image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:10.444956image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:10.530929image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:10.621187image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:10.706256image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:10.797167image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:10.887334image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:10.974143image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:11.056325image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:11.114740image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:11.174359image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:11.244654image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:11.302535image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:11.359949image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:11.446377image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:11.528574image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:11.594416image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:11.651438image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:11.706812image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:11.763323image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:11.820865image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:11.881302image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:11.955344image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:12.022950image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:12.083705image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:12.151109image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:12.217216image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:12.285554image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:12.344420image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:12.403876image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:12.461324image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:12.521484image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:12.579315image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:12.639358image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:12.698638image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:12.760684image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:12.829367image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:12.886774image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:12.950255image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:13.010029image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:13.076250image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:13.145993image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:13.642371image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:13.718346image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:13.775583image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:13.831910image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:13.890517image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:13.964075image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:14.033992image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:14.093089image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:14.164797image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:14.230868image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:14.299607image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:14.358725image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:14.417734image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:14.475233image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:14.535222image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:14.594625image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:14.653938image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:14.712030image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:14.778304image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:14.851415image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:14.909949image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:14.975196image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:15.033200image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:15.106343image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:15.188657image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:15.268820image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:15.347371image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:15.428993image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:15.489088image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:15.568344image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:15.628374image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:15.698042image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:15.779703image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:15.860492image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:15.936356image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:16.024985image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:16.113229image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:16.194137image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:16.279951image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:16.363433image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:16.443500image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:16.517629image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:16.576050image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:16.634418image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:16.703764image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:16.786929image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:16.844495image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:16.903267image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:16.989205image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:17.069732image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:17.147955image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:17.224902image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:17.308785image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:17.386542image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:17.458973image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:17.539256image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:17.614518image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:17.701745image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:17.778146image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:17.855735image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:17.936274image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:18.010317image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:18.083822image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:18.163336image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:18.243901image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:18.321347image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:18.394421image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:18.473531image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:18.548868image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:18.623668image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:18.701807image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:18.768276image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T12:15:18.839055image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-01-24T12:15:44.007257image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-01-24T12:15:44.287424image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-01-24T12:15:44.555777image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Missing values

2021-01-24T12:15:19.668187image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-01-24T12:15:22.862994image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-01-24T12:15:27.825739image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-01-24T12:15:30.588169image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

study_nodateabdominal_painabdominal_tendernessageagitatedalbaltanorexiaascitesastbleedingbleeding_gibleeding_gumbleeding_mucosalbleeding_nosebleeding_severebleeding_skinbleeding_urinebleeding_vaginalbleeding_vensitebody_temperaturebreathcare_typecns_abnormalcns_abnormal_signscompressionconjunctival_injectioncreatine_kinasecryoprecipitatecrystalloiddbpdehydrationdiarrhoeaevent_admissionevent_dischargeevent_enrolmentevent_onsetevent_shockffpgcs_eye_movementgcs_motor_responsegcs_verbal_responsegendergum_packinghaematocrit_highhaematocrit_maxhaematocrit_nohaematocrit_percentheighthepatomegalyicd_codeiggigg_interpretationigmigm_interpretationimpairmentjaundicelathargy_severeliver_acuteliver_involvedlymphocyteslymphocytes_percentmonocytesmonocytes_percentmovementnasal_packingneutrophilsneutrophils_percentns1_platelia_analyte_interpretationoedema_pulmonaryoutcomeparental_fluidpcr_dengue_interpretationpcr_dengue_loadpcr_dengue_serotypeperfusionplatelet_minplatelet_noplateletspleural_effusionpltpulsepulse_statusrbcrespiratory_distressrestlessnesssbpserology_interpretationshockshock_multipleskin_clammyskin_flushskin_rashvomitingwbcweightcomplications
01-00012010-10-17 05:00:00NaNNaN11.0NaNNaNNaNFalseNaNNaN<NA>FalseNaNFalseNaNNaNFalseFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTrueNaNNaNNaNNaNNaNMaleNaNNaNNaNNaNNaN144.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFull recoveryNaNLab-confirmed DengueNaN<LODNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNNaNNaN34.0False
11-00012010-10-19 00:00:00NaNNaN11.0NaN41.769.0FalseNaN147.0<NA>FalseNaNFalseNaNNaNFalseFalseNaNNaNNaNNaNNaNNaNNaNNaNNaN155.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNMaleNaNNaNNaNNaN37.8144.0NaNNaN3.00Negative7.02NegativeNaNNaNNaNNaNNaN1.5128.10.315.8NaNNaN3.5265.5NaNNaNFull recoveryNaNLab-confirmed DengueNaN<LODNaNNaNNaNNaNNaN110.0NaNNaNNaNNaNNaNNaNProbable SecondaryFalseFalseNaNNaNNaNNaN5.3734.0False
21-00012010-10-19 08:00:00TrueFalse11.0NaNNaNNaNFalseNaNNaN<NA>FalseNaNFalseNaNNaNFalseFalseNaNNaN36.8NaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNFalseNaNNaNTrueNaNNaNNaNNaNNaNNaNMaleNaNNaNNaNNaNNaN144.0FalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNPositiveNaNFull recoveryNaNLab-confirmed Dengue80710000.0<LODNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNFalseFalseTrueNaN34.0False
31-00012010-10-20 00:00:00NaNNaN11.0NaNNaNNaNFalseNaNNaN<NA>FalseNaNFalseNaNNaNFalseFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTrueNaNNaNNaNTrueNaNNaNNaNNaNMaleNaNNaNNaNNaNNaN144.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFull recoveryNaNLab-confirmed DengueNaN<LODNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTrueFalseNaNNaNNaNNaNNaN34.0False
41-00012010-10-21 00:00:00FalseNaN11.0NaNNaNNaNFalseNaNNaNFalseFalseNaNFalseNaNNaNFalseFalseNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNMaleNaNNaNNaNNaNNaN144.0NaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFull recoveryNaNLab-confirmed DengueNaN<LODNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNFalseFalseFalseNaNNaNFalseNaN34.0False
51-00012010-10-22 00:00:00NaNNaN11.0NaNNaNNaNFalseNaNNaN<NA>FalseNaNFalseNaNNaNFalseFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNMaleNaNNaNNaNNaNNaN144.0NaNNaN22.58Positive21.40PositiveNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFull recoveryNaNLab-confirmed DengueNaN<LODNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNNaNNaN34.0False
61-00012010-10-24 00:00:00NaNNaN11.0NaNNaNNaNFalseNaNNaN<NA>FalseNaNFalseNaNNaNFalseFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNMaleNaNNaNNaNNaNNaN144.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFull recoveryNaNLab-confirmed DengueNaN<LODNaN34.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNNaNNaN34.0False
71-00012010-10-25 00:00:00NaNNaN11.0NaNNaNNaNFalseFalseNaN<NA>FalseNaNFalseNaNNaNFalseFalseNaNNaNNaNNaNLevel 1NaNNaNFalseNaNNaNFalseFalseNaNFalseNaNNaNTrueNaNNaNNaNFalseNaNNaNNaNMaleFalseFalse45.017.0NaN144.0NaNA913NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNNaNFalseFull recoveryNaNLab-confirmed DengueNaN<LODTrueNaN2.0FalseFalseNaN110.0WeakFalseNaNNaN90.0NaNFalseFalseNaNNaNNaNFalseNaN34.0False
81-00022010-10-17 12:00:00NaNNaN1.0NaNNaNNaNFalseNaNNaN<NA>FalseNaNFalseNaNNaNFalseFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTrueNaNNaNNaNNaNNaNMaleNaNNaNNaNNaNNaN68.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNLab-confirmed DengueNaN<LODNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNNaNNaN9.5False
91-00022010-10-19 00:00:00NaNNaN1.0NaN41.814.0FalseNaN48.0<NA>FalseNaNFalseNaNNaNFalseFalseNaNNaNNaNNaNNaNNaNNaNNaNNaN115.0NaNNaNNaNNaNNaNTrueNaNNaNNaNNaNNaNNaNNaNNaNMaleNaNNaNNaNNaN34.368.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN5.0739.51.2810.0NaNNaN6.2648.8NaNNaNNaNNaNLab-confirmed DengueNaN<LODNaNNaNNaNNaNNaN178.0NaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNNaN12.829.5False

Last rows

study_nodateabdominal_painabdominal_tendernessageagitatedalbaltanorexiaascitesastbleedingbleeding_gibleeding_gumbleeding_mucosalbleeding_nosebleeding_severebleeding_skinbleeding_urinebleeding_vaginalbleeding_vensitebody_temperaturebreathcare_typecns_abnormalcns_abnormal_signscompressionconjunctival_injectioncreatine_kinasecryoprecipitatecrystalloiddbpdehydrationdiarrhoeaevent_admissionevent_dischargeevent_enrolmentevent_onsetevent_shockffpgcs_eye_movementgcs_motor_responsegcs_verbal_responsegendergum_packinghaematocrit_highhaematocrit_maxhaematocrit_nohaematocrit_percentheighthepatomegalyicd_codeiggigg_interpretationigmigm_interpretationimpairmentjaundicelathargy_severeliver_acuteliver_involvedlymphocyteslymphocytes_percentmonocytesmonocytes_percentmovementnasal_packingneutrophilsneutrophils_percentns1_platelia_analyte_interpretationoedema_pulmonaryoutcomeparental_fluidpcr_dengue_interpretationpcr_dengue_loadpcr_dengue_serotypeperfusionplatelet_minplatelet_noplateletspleural_effusionpltpulsepulse_statusrbcrespiratory_distressrestlessnesssbpserology_interpretationshockshock_multipleskin_clammyskin_flushskin_rashvomitingwbcweightcomplications
808896-10142013-12-15 00:00:00FalseNaN15.0NaNNaNNaNFalseNaNNaNFalseFalseNaNFalseNaNNaNFalseFalseNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNMaleNaNNaNNaNNaNNaN179.0NaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNTrueNaNNaNNaNNaNNaNNaNNaNLab-confirmed DengueNaN<LODNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNFalseFalseFalseNaNNaNFalseNaN67.0False
808906-10152013-12-10 15:30:00NaNNaN9.0NaNNaNNaNFalseNaNNaN<NA>FalseNaNFalseNaNNaNFalseFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTrueNaNNaNNaNNaNNaNFemaleNaNNaNNaNNaNNaN135.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNLab-confirmed DengueNaN<LODNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNNaNNaN23.0False
808916-10152013-12-12 00:00:00NaNNaN9.0NaN45.669.0FalseNaN93.0<NA>FalseNaNFalseNaNNaNFalseFalseNaNNaNNaNNaNNaNNaNNaNNaNNaN85.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFemaleNaNNaNNaNNaN42.0135.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.419.20.33.7NaNNaN5.877.1NaNNaNNaNNaNLab-confirmed DengueNaN<LODNaNNaNNaNNaNNaN180.0NaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNNaN7.523.0False
808926-10152013-12-12 10:35:00TrueFalse9.0NaNNaNNaNFalseNaNNaN<NA>FalseNaNFalseNaNNaNFalseFalseNaNNaN37.4NaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNTrueNaNNaNTrueNaNNaNNaNNaNNaNNaNFemaleNaNNaNNaNNaNNaN135.0FalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNegativeNaNNaNNaNLab-confirmed Dengue0.0<LODNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNFalseFalseTrueNaN23.0False
808936-10152013-12-14 00:00:00FalseNaN9.0NaNNaNNaNFalseNaNNaNFalseFalseNaNFalseNaNNaNFalseFalseNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFemaleNaNNaNNaNNaNNaN135.0NaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNTrueNaNNaNNaNNaNNaNNaNNaNLab-confirmed DengueNaN<LODNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNFalseFalseFalseNaNNaNFalseNaN23.0False
808946-10152013-12-14 14:00:00NaNNaN9.0NaNNaNNaNFalseNaNNaN<NA>FalseNaNFalseNaNNaNFalseFalseNaNNaN37.5NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFemaleNaNNaNNaNNaNNaN135.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNLab-confirmed DengueNaN<LODNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNNaNNaN23.0False
808956-10152013-12-15 00:00:00TrueNaN9.0NaNNaNNaNFalseNaNNaNFalseFalseNaNFalseNaNNaNFalseFalseNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFemaleNaNNaNNaNNaNNaN135.0NaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNLab-confirmed DengueNaN<LODNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNFalseFalseFalseNaNNaNFalseNaN23.0False
808966-10152013-12-16 00:00:00FalseNaN9.0NaNNaNNaNFalseNaNNaNFalseFalseNaNFalseNaNNaNFalseFalseNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFemaleNaNNaNNaNNaNNaN135.0NaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNLab-confirmed DengueNaN<LODNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNFalseFalseFalseNaNNaNFalseNaN23.0False
808976-10152013-12-16 08:00:00NaNNaN9.0NaNNaNNaNFalseNaNNaN<NA>FalseNaNFalseNaNNaNFalseFalseNaNNaN37.3NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFemaleNaNNaNNaNNaNNaN135.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNLab-confirmed DengueNaN<LODNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNNaNNaN23.0False
808986-10152013-12-17 00:00:00FalseNaN9.0NaNNaNNaNFalseNaNNaNFalseFalseNaNFalseNaNNaNFalseFalseNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFemaleNaNNaNNaNNaNNaN135.0NaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNTrueNaNNaNNaNNaNNaNNaNNaNLab-confirmed DengueNaN<LODNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNFalseFalseFalseNaNNaNFalseNaN23.0False